PALGRAVE STUDIES IN DIGITAL BUSINESS & ENABLING TECHNOLOGIES *SERIES EDITORS*: THEO LYNN · PIERANGELO ROSATI

# **Disrupting Buildings**

Digitalisation and the Transformation of Deep Renovation

*Edited by* Theo Lynn · Pierangelo Rosati Mohamad Kassem · Stelios Krinidis Jennifer Kennedy

# Palgrave Studies in Digital Business & Enabling Technologies

Series Editors Theo Lynn Irish Institute of Digital Business DCU Business School Dublin, Ireland

> Pierangelo Rosati Business School Dublin City University Malahide, Ireland

This multi-disciplinary series will provide a comprehensive and coherent account of cloud computing, social media, mobile, big data, and other enabling technologies that are transforming how society operates and how people interact with each other. Each publication in the series will focus on a discrete but critical topic within business and computer science, covering existing research alongside cutting edge ideas. Volumes will be written by feld experts on topics such as cloud migration, measuring the business value of the cloud, trust and data protection, fntech, and the Internet of Things. Each book has global reach and is relevant to faculty, researchers and students in digital business and computer science with an interest in the decisions and enabling technologies shaping society. More information about this series at http://www.palgrave.com/gp/series/16004.

# Theo Lynn Pierangelo Rosati Mohamad Kassem Stelios Krinidis • Jennifer Kennedy Editors

# Disrupting Buildings

Digitalisation and the Transformation of Deep Renovation

*Editors* Theo Lynn Irish Institute of Digital Business DCU Business School Dublin City University Dublin, Ireland

Mohamad Kassem School of Engineering Newcastle University Newcastle upon Tyne, UK

Jennifer Kennedy Irish Institute of Digital Business DCU Business School Dublin City University Dublin, Ireland

Pierangelo Rosati J.E. Cairnes School of Business and Economics University of Galway Galway, Ireland

Stelios Krinidis Information Technologies Institute Centre for Research & Technology Hellas (CERTH) Thessaloniki, Greece

Department of Management Science and Technology International Hellenic University Kavala, Greece

ISSN 2662-1282 ISSN 2662-1290 (electronic) Palgrave Studies in Digital Business & Enabling Technologies ISBN 978-3-031-32308-9 ISBN 978-3-031-32309-6 (eBook) https://doi.org/10.1007/978-3-031-32309-6

© The Editor(s) (if applicable) and The Author(s) 2023. This book is an open access publication.

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# Acknowledgements

This book was partially funded by the European Union's Horizon 2020 Research and Innovation Programme through the RINNO project (https://rinno-h2020.eu/) under Grant Agreement 892071, and the Irish Institute of Digital Business.

# Book Description

The world's extant building stock accounts for a signifcant portion of worldwide energy consumption and greenhouse gas emissions. In 2020, buildings and construction accounted for 36% of global fnal energy consumption and 37% of energy-related CO2 emissions. The European Union (EU) estimates that up to 75% of the EU's existing building stock has poor energy performance, 85–95% of which will still be in use in 2050.

To meet the goals of the Paris Agreement on Climate Change will require a transformation of construction processes and deep renovation of the extant building stock. The World Economic Forum, World Business Council for Sustainable Development, and the European Commission are amongst the many global organisations that recognise the important role ICTs can play in construction, renovation, and maintenance, as well as supporting the incentivisation and fnancing of deep renovation. Technologies such as sensors, big data analytics and machine learning, building information modelling (BIM), digital twinning, simulation, robots, cobots and unmanned autonomous vehicles (UAVs), additive manufacturing, smart contracts, and the Internet of Things are transforming the deep renovation process, improving sustainability performance, and developing new services and markets.

This book defnes a deep renovation digital ecosystem for the twentyfrst century, providing a state-of-the art review of current literature, suggesting avenues for new research, and offering perspectives from business, technology, and industry.

# Contents


x Contents


**Index** 173

# Notes on Contributors

**Mazen J. Al-Kheetan** is an Assistant Professor in the Department of Civil and Environmental Engineering at Mutah University, Jordan. He is also an associate editor at the Proceedings of the Institution of Civil Engineers— Transport, UK. He previously served as the head of the Department of Civil and Environmental Engineering at Mutah University, Jordan.

**Marco Arnesano** is Associate Professor of Mechanical and Thermal Measurements and coordinator of Industrial Engineering at eCampus University, Italy. He is also the co-founder of LIS (Live Information System), a startup company developing BIM-based solutions for buildings' digitalisation.

**Ioannis Brilakis** is Laing O'Rourke Professor of Construction Engineering and the director of the Construction Information Technology Laboratory in the Department of Engineering at the Division of Civil Engineering, University of Cambridge, UK.

**Mehdi Chougan** is a Marie Skłodowska-Curie Research Fellow in the Department of Civil and Environmental Engineering at Brunel University London, UK. His research focuses on cementitious composite materials, especially in graphene-engineered cementitious composites, and additive manufacturing of alkali-activated cementitious composites.

**Mark Cummins** is Professor of Financial Technology at the University of Strathclyde, UK. His research interests include fnancial technology (FinTech), quantitative fnance, energy and commodity fnance, sustainable fnance, and model risk management.

**Borja García de Soto** is Assistant Professor of Civil and Urban Engineering at New York University Abu Dhabi (NYUAD), UAE, and a Global Network Assistant Professor in the Department of Civil and Urban Engineering at the Tandon School of Engineering, New York University (NYU), USA. He is the director of the S.M.A.R.T. Construction Research Group at NYUAD and his research focuses in the areas of automation and robotics in construction, cybersecurity in the AEC industry, artifcial intelligence, lean construction, and BIM.

**Asimina Dimara** is a Research Assistant at Centre for Research & Technology Hellas/Information Technologies Institute (CERTH/ITI), Greece. She holds an MSc by research in Intelligent Computer Systems from the University of the Aegean and is currently undertaking a PhD in Intelligent Computer Systems from the University of the Aegean.

**Omar Doukari** is Research Fellow in Construction Informatics at the University of Northumbria Newcastle, UK. He completed his PhD in Computer Science and Artifcial Intelligence, focusing on spatial information representation, modelling, and reasoning.

**Antonia Egli** is the Communication & Dissemination Manager for the H2020-funded deep renovation project, RINNO, and Research Fellow at the Irish Institute of Digital Business and *safe*food. As a postgraduate researcher, she focuses on identity, stigma, and the spread and infuence of misinformation within the vaccine discourse on social media.

**Seyed Hamidreza Ghaffar** is Professor of Civil Engineering. He is a Chartered Civil Engineer (CEng, MICE), a Member of the Institute of Concrete Technology (MICT), and a Fellow of Higher Education Academy (FHEA). He is the founder and director of Additive Manufacturing Technology in Construction (AMTC) Research Centre at Brunel University London, UK.

**David Greenwood** is Professor of Construction Management at Northumbria University, UK, and director of BIM Academy. He has written widely and delivered consultancy around the world.

**Zhiqi Hu** is an early stage researcher under the Fellowship Marie Skłodowska-Curie Actions and a PhD candidate in the Department of Engineering at the Construction Information Technology Laboratory, University of Cambridge, UK.

**Dimosthenis Ioannidis** is a Senior Researcher at the Information Technologies Institute of the Centre for Research & Technology Hellas and Lecturer in Interdepartmental Postgraduate Programs at the Aristotle University of Thessaloniki, Greece.

**Mohamad Kassem** is Professor of Digital Construction Management at Newcastle University, UK. He has established expertise in development and application of digital and data-centric tools and methods in construction and infrastructure management. In this domain, he authored over 130 papers and successfully secured and completed multi-million-pound research and innovation grants.

**Jennifer Kennedy** is a Postdoctoral Researcher at the Irish Institute of Digital Business at DCU Business School. Dr Kennedy specialises in knowledge processes with a specifc focus on how tacit knowledge is transferred between novice and experts in the workplace.

**Paraskevas Koukaras** is a Postdoctoral Researcher at the Information Technologies Institute of the Centre for Research & Technology Hellas and Lecturer in Postgraduate Programs at the School of Science and Technology, International Hellenic University, Greece.

**Stelios Krinidis** is an Assistant Professor in the Department of Management Science & Technology at the International Hellenic University, Greece, and a postdoctoral researcher at the Information Technologies Institute of the Centre for Research & Technology Hellas.

**Theo Lynn** is Full Professor of Digital Business at Dublin City University, Ireland, and co-director of the Irish Institute of Digital Business. He was formerly the principal investigator (PI) of the Irish Centre for Cloud Computing and Commerce, and director of the LINK Research Centre. Lynn specialises in the role of digital technologies in transforming business processes and society with a specifc focus on cloud computing, social media, and data science.

**Silvia Angela Mansi** is a PhD student in Science Applied to Wellness and Sustainability at eCampus University, Italy. Her research activity is focused on sensors for indoor comfort measurement with multi-domain approaches.

**Yuandong Pan** is a PhD student at the Chair of Computational Modeling and Simulation and Institute for Advanced Study, Technical University of Munich, Germany.

**Alessandro Pracucci** is Innovation Manager at Focchi Group, Italy. He has expertise in multidisciplinary research particularly in technological, digital, and sustainability relations. He leads the Department of Innovation at Focchi Group, investigating new growth opportunities for the company.

**SeyedReza RazaviAlavi** is an assistant professor at Northumbria University, UK. Prior to joining Northumbria University, he was a postdoctoral fellow in the Construction Simulation research group at the University of Alberta, and worked fve years as a project management consultant in Canada.

**Pierangelo Rosati** is Associate Professor of Digital Business and Society at University of Galway, Ireland. His research interests include digital business, business value of IT, FinTech, blockchain, cloud computing, and cyber security.

**Muammer Semih Sonkor** is a Research Assistant at New York University Abu Dhabi (NYUAD), UAE, and a PhD student at New York University (NYU), USA. He worked on several large-scale construction projects before attending the European Master's Program in Building Information Modeling (BIM A+). He conducts research focusing on cybersecurity in construction as a part of the S.M.A.R.T. Construction Research Group at NYUAD.

**Christos Tjortjis** is the Dean of the School of Science and Technology at the International Hellenic University, Greece, director for 5 MSc programmes, and Associate Professor of Knowledge Discovery and Software Engineering Systems.

**Dimitrios Tzovaras** is a Senior Researcher and the president of the Board of the Centre for Research & Technology Hellas, Greece.

**Laura Vandi** is a Project Manager in the Department of Innovation at Focchi Group, Italy. She manages and develops internal and European projects regarding sustainability in the construction sector with particular focus on retroftting, technologies, and circular economy. She spent years abroad and worked in architectural frms.

# Abbreviations



# List of Figures


# List of Tables


#### xx List of Tables


# Deep Renovation: Defnitions, Drivers and Barriers

*Theo Lynn, Pierangelo Rosati, and Antonia Egli*

**Abstract** This chapter defnes the key elements of the deep renovation life cycle. Investment in deep renovation is driven by various rationales, including societal, economic, environmental, energy security, quality, opportunistic, and catalytic motivations and benefts. At the same time, both deep renovation and digital technology adoption to support deep renovation are impacted by challenges presented in humans, organisational processes, technologies and external environments. This chapter explores the key drivers and barriers to deep renovation and associated digitalisation. It establishes the motivation for the remainder of the book.

P. Rosati

T. Lynn (\*) • A. Egli Irish Institute of Digital Business, DCU Business School, Dublin City University, Dublin, Ireland e-mail: theo.lynn@dcu.ie; antonia.egli@dcu.ie

J.E. Cairnes School of Business and Economics,

University of Galway, Galway, Ireland

e-mail: pierangelo.rosati@universityofgalway.ie

<sup>©</sup> The Author(s) 2023

T. Lynn et al. (eds.), *Disrupting Buildings*, Palgrave Studies in Digital Business & Enabling Technologies, https://doi.org/10.1007/978-3-031-32309-6\_1

**Keywords** Deep renovation • Energy effciency • Residential buildings • Renovation life cycle • Adoption • Barriers to adoption

### 1.1 Introduction

The 2022 Intergovernmental Panel on Climate Change (IPCC) assessment suggests that climate-resilient development is already challenging at current warming levels and that the window for action to address climate change is narrowing. Restricting warming to around 2°C (3.6°F) still requires global greenhouse gas (GHG) emissions to peak before 2025 at the latest and be reduced by a quarter by 2030 (IPCC, 2022). This is a signifcant challenge. In the words of UN Secretary-General António Guterres, "the climate emergency is a race we are losing, but it is a race we can win" (UN, 2019).

The European Union (EU) is not sitting idle. As part of the European Green Deal, the EU has raised its ambition to reduce GHG emissions by 2030 from its previous target of 40% to at least 55% below 1990 levels, as well as increasing the share of renewable energy by 32%, and improving energy effciency by 32.5% (European Commission, 2022). The renovation of EU building stock is particularly critical in supporting these goals. Buildings are among the most signifcant sources of energy use within the EU: existing structures currently account for 40% of all energy consumption and 36% of GHG emissions (European Commission, 2020a). In particular, it is estimated that over 75% of the EU's residential building stock has poor energy performance, the majority of which will be still in use by 2050 (European Commission, 2021a). To meet its climate change goals, the EU seeks to achieve a decarbonised EU building stock by 2050. To achieve this, it has recently put in place measures to consolidate its existing goals, encourage the use of digital technologies and smart applications in building operations, and strengthen the links between achieving higher renovation rates, funding and energy performance certifcation (European Commission, 2021a). Deep renovation is key to achieving this goal.

The remainder of this chapter will explore narrow and broad defnitions of deep renovation including the rationales for undertaking deep renovation. Recent research by Lynn et al. (2021) suggests such rationales not merely are related to environmental sustainability but include a wide range of different stakeholder motivations including economic, energy security and opportunistic rationales, amongst others. Notwithstanding these rationales, the widespread deep renovation of building stock, particularly in a constrained time frame, faces signifcant barriers not least human, organisational, technological and environment context challenges. We discuss how these barriers may surface across the life cycle of a deep renovation project. Advances in technologies, not least information and communications technologies (ICTs), are central to accelerating the renovation life cycle and overcoming the existing barriers to deep renovation. We conclude with a summary of the remainder of this book which looks at the main digital innovations disrupting and transforming the construction sector.

#### 1.2 Deep Renovation

"Deep renovation" has become somewhat of a buzzword in recent years, albeit an obscure one. There remains little consensus on the term's defnition and, although widely adopted in academia, industry and legislation, defnitions vary signifcantly on local, regional and international levels (Shnapp et al., 2013). While deep renovation (sometimes referred to as deep energy renovation, deep retroft or deep refurbishment) may be defned simply as renovation efforts which capture the "full economic energy effciency potential of improvement works […] of existing buildings" and lead to high energy performance levels (Shnapp et al., 2013), the core concept of deep renovation is categorised into *broad* and *narrow* defnitions:


D'agostino et al. (2017) take a more quantitative approach, categorising deep renovation efforts by performance impact as presented in Table 1.1. This offers a relative numeric classifcation of deep renovation efforts, although an exact quantitative reference value for deep renovation energy reductions remains unavailable (D'Oca et al., 2018).

Deep renovation involves the use of multiple energy-saving measures. Bruel et al. (2013) summarise these measures as (1) energy-effcient building elements such as windows, heating, ventilation and air conditioning (HVAC), air fltration, lighting and appliances; (2) renewable energy sources like solar hot water, solar photovoltaic (PV) panels, passive solar energy, shading, wind, heat pumps, biomass and biogas; and (3)


**Table 1.1** Categorisations of deep renovation measures

community energy sources such as district heating systems. Each of these measures alone improves energy performance in buildings and may be employed in combination with traditional technology and construction solutions (D'Oca et al., 2018). However, deep renovation is distinct from other energy-effcient retrofts in that these elements become *fully integrated* within the renovation process.

# 1.3 Rationales and Benefits of Deep Renovation

In 2018, EU renovation rates barely exceeded 1% and were signifcantly below the objectives set in the Energy Effciency Directive (Directive 2012/27/EU) and the revised Energy Performance Building Directive (Directive 2018/844). Only 11% of the EU building stock undergoes renovation on a yearly basis (European Commission, 2021a). Reaching the 2030 and 2050 goals requires a signifcant acceleration and greater understanding of what drives stakeholders to adopt and implement a deep renovation strategy. An attempt at this is made in Table 1.2.

Aside from advancing building quality and area net-worth in comparison to other buildings through state-of-the-art aesthetic, safe and easy-touse building elements, deep renovation reinforces economic stimuli in the form of employment and reduced reliance on international energy imports


**Table 1.2** Stakeholder rationales towards adopting deep renovation practices

(*continued*)


#### **Table 1.2** (continued)

(*continued*)


**Table 1.2** (continued)

(Jochem & Madlener, 2003; Baek & Park, 2012; Bruel et al., 2013; Ferreira & Almeida, 2015; D'Oca et al., 2018; European Commission, 2020). Currently, approximately 34 million Europeans are impacted by energy poverty or the inability to afford adequate heating or lighting (European Commission, 2020b). As such, deep renovation supports citizens in participating in a greener society frst-hand while simultaneously improving energy security, health and accessibility for society's most vulnerable citizens (Baek & Park, 2012; Bruel et al., 2013; Ferreira & Almeida, 2015; European Commission, 2020). Deep renovation works lastly deliver improved consumer service on public, community and commercial levels (Jochem & Madlener, 2003; Baek & Park, 2012; Guerra-Santin et al., 2017; Klumbyte et al., 2020).

If properly integrated, deep renovation efforts create resilient and green living spaces while promoting high energy performance and lower waste and pollution levels (Baek & Park, 2012; Bruel et al., 2013; Ferreira & Almeida, 2015; Haase et al., 2020). From a wider perspective, such efforts lead to improved quality of life for building occupants, increased revenues and decreased technological and operational costs through superior products and services, improved security, quality and control over full project life cycles, and more durable buildings in the long term (Mainali et al., 2021). Perhaps most importantly, deep renovation may positively infuence public attitudes towards climate change mitigation works, substitute existing, climate-damaging methods in the traditionally conservative construction sector and improve the uptake of novel and existing ClimateTech and CleanTech measures (Baek & Park, 2012; Mainali et al., 2021).

## 1.4 Barriers to Deep Renovation

Prior literature presents an extensive range of theoretical lenses by which to explore technology adoption and use, typically from an adopter-centred or innovation or organisation-centred perspective. These lenses are summarised in Table 1.3.


**Table 1.3** Theoretical overview of technology adoption and use

(*continued*)


**Table 1.3** (continued)

Although the individual arguments for shortcomings in acceptance towards deep renovation measures lie beyond the scope of this chapter, it is worth noting that the success of deep renovation efforts is impacted by adopter-centred factors, technology-related factors, organisational factors and external environmental factors. The following sections elaborate on these potential reasons for failure.

#### *1.4.1 Human Barriers to Deep Renovation Adoption and Use*

Barriers to accepting, supporting and adopting climate-friendly technologies and practices in buildings are manifold. Hesitancy can be traced back to restrictive social norms and household characteristics, short-termism and lack of clarity surrounding the negative consequences of climate change, as well as inadequate knowledge or reservations about the existence or use of new technologies (Van Raaij & Verhallen, 1983; Curtis et al., 1984; Scott, 1997; Abrahamse et al., 2005; Organisation for Economic Co-operation and Development, 2011; Mills & Schleich, 2012; Huebner et al., 2013; Giraudet, 2020). Demographics such as age, education, household composition and geographical location have equally been shown to affect the adoption of energy effciency technologies. For example, Mills and Schleich (2012) fnd that families with young children (unlike elderly household members) are more likely to adopt energyeffcient technologies, as are those with higher education levels. Interestingly, data suggests a high degree of country heterogeneity with respect to adoption, use and attitudes towards household energy-effcient technologies and energy conservation practices (Mills & Schleich, 2012).

In their ethnographic study of the occupants and users of a multidwelling residential building in Italy, Prati et al. (2020) fnd that enhanced quality of life and long-term fnancial savings were the primary motivators for accepting and supporting deep renovation projects for tenants. However, the economic burden does not fall on tenants, suggesting a need for a *multi-stakeholder approach* to deep renovation projects particularly where there is a divergence in ownership and occupancy. While levels of normative legitimacy may be relatively high amongst tenants (considering the largely accepted moral obligation of preserving the environment), pragmatic legitimacy may be restrained by conficts between building owners' self-interest, perceived utility, and fnancial and time requirements of renovation works. Research suggests that barriers infuencing the selfinterest and utility involved in deep renovation measures include occupant disturbance and a lack of awareness, understanding and trust in deep renovation and new technologies (D'Oca et al., 2018; Prati et al., 2020; European Commission, 2020). Further individual adopter factors include performance expectancy, effort expectancy and social infuence (Fishbein & Ajzen, 1977; Ajzen, 1991; Davis, 1985, 1989; Venkatesh et al., 2003, 2012).

Psychological (and oftentimes geographical) distance to the climate crisis is a key barrier amongst consumers in mitigating the effects of climate change and maintaining pro-environmental behaviours (Spence et al., 2012). In one scenario, this may result in building owners and occupants failing to adopt energy management measures in an individual building and within the context of that building's location and climate. As a consequence, this usually leads to unnecessarily high energy and emissions levels (Jochem & Madlener, 2003). In this context, *short-termism* has had a particularly negative impact on the adoption of deep renovation projects. For example, there is a substantial literature base which acknowledges that the adoption of energy-effcient measures is related to cost (Curtis et al., 1984; Abrahamse et al., 2005; Organisation for Economic Co-operation and Development, 2011). Consumers are more likely to adopt low-cost or no-cost measures much unlike deep renovation projects. As Mills and Schleich (2012) note, such behavioural changes may only have transitory effects, while energy savings resulting from technology adoption tend to have more long-term effects. Consequently, although the adoption of energy-effcient technologies can have a signifcant impact on the wider environment, it does not necessarily compensate energy savers and thus presents a signifcant challenge in persuading the public to act (Mills & Schleich, 2012). Particularly in the context of multi-dwelling residential buildings, this may cause mismatches between individual needs and beliefs and those of the wider collective (D'Oca et al., 2018).

Notably, *solution aversion* to climate-friendly measures, which occurs when problems are ignored due to dissatisfaction with proposed solutions, may also impact openness towards deep renovation efforts (Campbell & Kay, 2014). *Tangible solution aversion* in particular applies to the deep renovation context. Poortinga et al. (2004), for example, warn that environmental attitudes may be too limited in explaining environmental behaviour and related technology adoption—particularly because addressing climate change results in tangible lifestyle changes for building occupants. For this reason, deep renovation solutions must pay attention to promoting the cost of non-action and life-quality benefts in ways that can be received by different audiences in different climate and building-type contexts.

#### *1.4.2 Technological Barriers to Deep Renovation Adoption and Use*

Technology-related adoption factors include, amongst others, innovation characteristics, availability, ease of use, compatibility, results demonstrability and quality-driven factors (Tornatzky & Fleischer, 1990; Rogers, 1995, 2003; Yusof et al., 2008). Key focus points over previous years have shifted from the technical suitability of deep renovation technologies primarily to the *integration* of energy-saving technologies throughout deep renovation projects (D'Oca et al., 2018). This includes building envelopes, HVAC systems and RES-powered systems (D'Oca et al., 2018). Today, the main technological challenge to deep renovation lies in the complexity associated with integrating technically viable, context-appropriate technologies according to desired outcomes and regulatory standards (Attia et al., 2017). Because of this, one could posit that meeting standards of deep renovation requirements, for example, the Passive House Standard, is less a matter of the technological state of the art, but rather technical awareness, availability and know-how (Innovate UK, 2013; De Gaetani et al., 2020). In its worst case, a lack thereof can lead to missed opportunities, inadequate performance and dissatisfaction with deep renovation as a concept.

The issue of integrating technologies into the building renovation process becomes particularly complex when one considers the abundance of domains, stakeholders and outbound dependencies to systems, regulations and geographical characteristics related to the deep renovation process. This is an issue of *interoperability*, that is, "the ability of two or more systems or components to exchange information and to use the information that has been exchanged" (ISO, 2013). The defnition of interoperability has morphed somewhat over time, initially used to describe "a feature of information systems that enabled information exchange" to any system which is able to collaborate with another system (Turk, 2020). Its value becomes evident in enabled communication, coordination, cooperation, collaboration and distribution (Grilo & Jardim-Goncalves, 2010). Unfortunately, the range of heterogeneous applications and systems used by different stakeholders varies across the project life cycle. An example of this is Building Information Modelling (BIM), which presents a plethora of varying software tools designed for energy simulation, planning and management (El Asmi et al., 2015; Arayici et al., 2018). Lacking interoperability (particularly when combined with the dynamic nature of construction projects) becomes an issue in that data fows and value generation are negatively affected by data mismatches, data quality issues and inconsistent sector standards and processes (Curry et al., 2013; Arayici et al., 2018; Shirowzhan et al., 2020). While interoperability with other systems, for example, Geographic Information System (GIS) and Augmented Reality (AR)/Virtual Reality (VR), has been increasingly prioritised, knowledge and practice gaps for integrating stateof-art technologies remain (Shirowzhan et al., 2020).

This is not to say that the technological status quo does not face quality or performance issues in itself (Attia et al., 2017). Primarily the adoption and use of software- or cloud-enabled solutions is inficted by poor on-site connectivity and latency, lack of integration across supply chains, inconsistent data fows and inadequate worker skills (Almaatouk et al., 2016; Bello et al., 2020). A further by-product of the Internet of Things or smart or otherwise connected products is copious volumes of data—all originating from end points with varying capabilities, connectivity levels, requirements and priorities. Due to the idiosyncrasies of individual buildings and living spaces, owners and occupiers, and the environment in which they are located, this requires taking into account both local and more global considerations (Venkatesh, 2008).

#### *1.4.3 Organisational Barriers to Deep Renovation Adoption and Use*

Organisation-related barriers to deep renovation include organisation size and structure, adequacy of resources, top management support and perceived indirect benefts (Tornatzky & Fleischer, 1990; Rogers, 1995, 2003; Yusof et al., 2008). Because of the multi-stakeholder nature of deep renovation projects, existing resources, technical competencies and innovation levels amongst management and operational teams vary and must be considered (Yusof et al., 2008). Resource allocation, fnancial investment and employee competency all have the potential to hinder deep renovation uptake. For example, research fnds that inadequately trained professionals and construction workers within the realm of energy effciency present a signifcant barrier to project success (Innovate UK, 2013; Attia et al., 2017; D'Oca et al., 2018; Vavallo et al., 2019).

From an organisational perspective, fnancial barriers are amongst the most highly cited in literature (Cooremans & Schönenberger, 2019; Bertoldi et al., 2021). This is accelerated by the complexity of deep renovation, particularly in multi-residential buildings such as social housing or other fragmented ownership models (D'Oca et al., 2018). Procurement policies which prioritise price over the quality of renovations, combined with high upfront investment costs and challenging access to funding, may negatively affect deep renovation efforts initiated by the public sector (European Commission, 2017; Van Oorschot et al., 2019; D'Oca et al., 2018; European Commission, 2020). In its worst case, this can result in project delays, underwhelming energy performance and heightened costs—fnally leading to reduced consumer trust in public sector efforts overall and specifcally deep renovation projects (D'Oca et al., 2018).

#### *1.4.4 External Environment Barriers to Deep Renovation Adoption and Use*

The external environment, including building and environmental regulations, policies and standards, heavily impacts deep renovation. Environmental factors encompass all external pressures on deep renovation initiatives, including regulatory, competitive and fnancial pressures, as well as related support from public bodies and partners (Tornatzky & Fleischer, 1990). For those involved in the supply chain, keeping up with changing regulatory requirements can be a signifcant challenge particularly under changing political administrations.

Legislation and regulation are highlighted as potentially obtrusive to deep renovation efforts in that these are often complex, unclear and timeconsuming (European Commission, 2017; D'Oca et al., 2018; European Commission, 2020). One reason for this is that the context of local governments, and more specifcally local energy issues, is often ignored in EU regulations or other intergovernmental treaties. Here, central governments are mainly targeted and expected to oversee the implementation of climate objectives (European Commission, 2017). Because they are responsible for the implementation of energy-saving measures, local entities have specifc insights into the barriers they face and must therefore become more closely involved in the development of deep renovation strategies, regulations and targets (European Commission, 2017). In a cross-European report, main local barriers to deep renovation were found to be primarily fscal and fnancial (i.e., referring to lack of technical skills for funding applications, poorly designed or lack of incentives, limited borrowing capacity, complex fnancial schemes and unfavourable accounting rules), followed by legislative and strategic barriers such as an incomplete overview of building stocks, limited training in deep renovation practices and lack of technical capacity required for such projects (European Commission, 2017). As previously identifed in Sect. 1.2, one fnal clear strategic barrier was deemed to be the lack of a uniform defnition of deep renovation itself (European Commission, 2017).

#### 1.5 Conclusion

This chapter introduces deep renovation, which involves renovation works that capture the full potential of energy- and cost-saving adjustments to existing buildings, along with its benefts and the human, technological, organisational and external environment barriers associated with deep renovation projects. Deep renovation has the potential to transform the construction and renovation industry in its integrated use of multiple energy-saving measures. Projects simultaneously offer relief for vulnerable residential consumer groups, further desperately needed climate-friendly and potentially net-zero energy practices, and heighten the long-term durability of buildings. Each chapter of this book is dedicated to exploring the impact of a specifc digital technology on the implementation and delivery of deep renovation projects. Chapter 2 is dedicated to embedded sensors, one of the (if not the most important) enabling technology in the digitalisation of deep renovation. In fact, the use of sensor networks and connectivity represents a key prerequisite for measuring, and therefore optimising, the energy performance of an existing building and for effcient construction management. This chapter presents the role of sensor networks in the feld of deep renovation, introduces the concept of smart buildings and smart homes and their main advantages and benefts, and highlights some of the main challenges and concerns associated with the use of sensor infrastructures which are mostly related to the volume, access and use of data captured by sensors on an ongoing basis.

Chapter 3 focuses on BIM, which leverages the large volume of data generated by sensor networks to manage "[…] the information on a project throughout its whole life cycle" (Hamil, 2022). Chapter 3 explores the evolution of BIM from its emergence in the early 1990s to recent developments and describes different BIM "dimensions". The chapter continues by presenting how BIM enables multi-criteria decision-making in the context of building renovation, and deep renovation more specifcally, and how it can help to identify, optimise, validate and communicate different renovation scenarios and corresponding costs, timelines and effectiveness. The chapter concludes with a discussion of two main sets of barriers to BIM adoption, namely interoperability and the lack of ontologies that are specifcally designed for renovation work which undermine the potential for process automation.

Another way of leveraging the vast amount of data generated by sensors is to develop models that evaluate the energy performance of an existing building and estimate how changes in external and internal conditions would affect such a performance. This technique is called Building Performance Simulation (BPS) and is the main topic of Chap. 4. More specifcally, this chapter provides an overview of the main approaches and applications of BPS in the context of deep renovation and discusses how to integrate simulations with real-time monitoring and diagnostic systems for building energy management and control.

Chapter 5 is dedicated to the application of Big Data and analytics in the deep renovation with a particular focus on Machine Learning and Artifcial Intelligence and the changes they have enabled in the various phases of the renovation project life cycle, from the renovation design to post-renovation monitoring and assessment. The chapter presents a series of use cases and applications of Big Data in construction and discusses the main advantages and benefts (e.g., alternative design automation, the development of accurate performance prediction models, higher effciency and reduced environmental impact of the renovation work), as well as the main barriers and challenges (human, technological and organisational) to the wider adoption of Big Data and analytics in deep renovation.

When it comes to capturing data about the physical structure of an existing building, detailed information can be gathered by adopting 3D scanning tools and techniques which enable the creation of a digital twin of the building. Chapter 6 introduces this novel technological paradigm in more detail, describes the main steps and approaches to creating digital twins and presents three main use cases for digital twins in the built environment, namely condition monitoring, facility management and environment simulation. The chapter concludes with a discussion of the main challenges associated with adopting and using digital twins which are mostly related to the high cost and effort required to create the digital twin.

Chapters 7 and 8 turn the attention to the construction phase of the renovation life cycle. In fact, Chap. 7 focuses on additive manufacturing (often referred to as 3D printing) which is the process of fabricating threedimensional objects following a specifc computer design. Additive manufacturing has attracted growing attention from the construction sector in recent years as it promises lower waste and costs, and it provides the opportunity to create complicated large-scale structures and integrate functional building elements such as pipes and storage units within the structure itself. These benefts are discussed in more detail alongside some practical challenges (e.g., equipment costs, skills and lack of standardisation) that are adversely impacting the diffusion of this technology.

Chapter 8 focuses on the use of intelligent equipment and robots (IER) in construction sites. This chapter discusses the maturity of IER technologies that are currently available in the market, describes how they can be used both on-site (e.g., inspection, construction and maintenance) and off-site (e.g., factories) and discusses the key concerns and barriers to adoption which are mostly related to high costs, lack of skills, humanrobot interactions and security.

The issue of security is not only relevant in the context of IER, but it is a recurring concern across the entire renovation life cycle. This topic is discussed in more depth in Chap. 9 which provides an overview of relevant cybersecurity frameworks, standards, guidelines and codes of practice. These include, for example, relevant International Organization for Standardization (ISO) and American Institute of Certifed Public Accountants (AICPA) standards, the NIST Framework for Improving Critical Infrastructure Cybersecurity, and the European Union Network and Information Systems (NIS) Directive. The chapter concludes by highlighting the need for a contingency approach to assess and manage cyber risk in the context of building renovation, as a one-size-fts-all approach may not be desirable or feasible given the variety of stakeholders involved in this kind of projects.

The fnal chapter discusses how novel fnancial technology (fntech) solutions such as crowdfunding, peer-to-peer lending and blockchainbased mechanisms such as tokenisation can help building owners and construction companies overcome one of the main barriers to deep renovation, access to capital. The chapter outlines the main advantages and benefts of these alternative sources of fnance, as well as the challenges associated with each of these funding mechanisms, and concludes with a call for further research on both demand side (fund seekers) and supply side (investors) incentives and dynamics or indeed on the responsibilities of platforms that enable and facilitate these transactions.

#### References


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Embedded Sensors, Ubiquitous Connectivity and Tracking

*Marco Arnesano and Silvia Angela Mansi*

**Abstract** The digitalisation of the deep renovation process and built environment is enabled by ubiquitous connectivity and monitoring of the environment itself, the artefacts and actors within it, and events that occur. Such monitoring is important for effcient construction management, dynamic peak demand reduction, affordability, and occupants' well-being. Sensor networks based on Internet of Things (IoT) technologies represent an important prerequisite for both optimising and redefning the stages of the building process to meet environmental challenges. This chapter provides an overview of how computation capabilities are being integrated into the physical environment and the role of sensor networks in the context of deep renovation. The key advantages and benefts of these technologies at the pre, during and post-renovation stages are discussed together with different use cases. The value of sensor network infrastructures and the legal and ethical implications of the use of such

M. Arnesano (\*) • S. A. Mansi

Università Telematica eCampus, Novedrate, Italy

e-mail: marco.arnesano@uniecampus.it; silviaangela.mansi@uniecampus.it

<sup>©</sup> The Author(s) 2023

T. Lynn et al. (eds.), *Disrupting Buildings*, Palgrave Studies in Digital Business & Enabling Technologies, https://doi.org/10.1007/978-3-031-32309-6\_2

### 2.1 Introduction

Sensors play a pivotal role in reducing buildings' energy demand and in reaching the near Zero Energy Building (nZEB) standard through deep renovation. From this perspective, buildings should become a system that "provides every occupant with productive, cost effective and environmentally approved conditions through continuous interaction among its elements" (Buckman et al., 2014, p. 96). The Internet of Things (IoT) represents the cornerstone in the defnition of a smart building (SB). Using sensor networks, SBs provide the possibility for monitoring and managing energy consumption and indoor environmental quality (IEQ) (Minoli et al., 2017).

This chapter aims to (1) defne the role of sensor networks in the feld of deep renovation, (2) summarise the main advantages and benefts as well as (3) the main challenges and concerns associated with the use of sensor infrastructures.

The remainder of this chapter is structured as follows. Section 2.2 introduces the key concepts for the defnition of construction sites, smart buildings and services based on sensor networks. Given the importance of IoT for the deployment of innovative sensing solutions in both construction sites and smart buildings; Sect. 2.2 also includes the description of the IoT architecture together with the main communication technologies. Then, Sect. 2.3 presents various sensing use cases for the construction and renovation stages and Sect. 2.4 presents the application of sensors in smart buildings. This is followed by a discussion of ethical and legal aspects related to the use of sensor data in Sect. 2.5 before concluding.

### 2.2 Key Definitions, Technologies and Approaches

#### *2.2.1 The Role of Sensors on Construction Sites*

Effective monitoring of construction sites allows managers to record progress at different stages and ensure that the project stays on schedule. With the advent of sensor technology and IoT, several activities in a construction site can be monitored automatically and in real-time. The interaction between multiple stakeholders can provide a better understanding of the status of different construction activities while improving productivity and saving time and cost. Through IoT, communication and positioning technologies can also improve safety (Zhao et al., 2021) and waste management (Sartipi, 2020).

3D point cloud data represents the main approach *to mapping and monitoring* construction progress in real-life large-scale projects. This measurement system can create a digital twin of the building and identify objects present on site, capturing their external surface. Improved knowledge can be obtained with *sensor integration*, a common procedure to integrate data from different sensors to improve the quality and the accuracy of information acquired by each sensor individually. Typically, the integration involves data fusion between *mapping* sensors and *positioning and communication* sensors (Moselhi et al., 2020). While positioning sensors measure the distance travelled by a body starting from its reference position, communication sensors allow the communication between different devices. For instance, communication sensors, when integrated with positioning and mapping sensors, can be used to enhance the outdoor tracking of resources (Domdouzis et al., 2007) in a supply chain management system (Rajendranath, 2011) and the construction safety management (Park et al., 2019). The main positioning technologies are presented in Table 2.1.

Different approaches can be applied for real-time monitoring construction progress. Table 2.2 summarises the main monitoring methodologies that are currently used in real-life construction sites for understanding a particular scene, positioning objects and tracking objects.


**Table 2.1** Positioning sensors in construction


**Table 2.2** Monitoring methodologies for the construction site

#### *2.2.2 Smart Buildings and Smart Homes*

A smart building is an intelligent structure that is "*[…] expected to address both intelligence and sustainability issues by utilising computer and intelligent technologies to achieve the optimal combinations of overall comfort level and energy consumption*" (Wang et al., 2012, p. 260). An SB adapts its operation and physical form to a particular event before the event happens while maintaining its energy effciency and occupant satisfaction (Buckman et al., 2014). SBs require many stakeholders and a lot of interconnectedembedded devices, automated systems and wireless technologies to be capable of communicating with the internal and external environment. Sensors play a pivotal role for an SB because of the need of measuring several quantities, belonging to different domains, which are required for each service deployed in an SB. Just to mention a few, electricity and heat


**Table 2.3** The main services and requirements of a smart building

meters are required for energy monitoring, and environmental sensors (temperature, relative humidity, light, CO2) are required for occupants' comfort measurement and control. Table 2.3 summarises the main functions and sensing requirements.

A *Smart Home* is the SB declination in a residential context. It is an environment equipped with technologies that make occupants' lives more convenient while preserving energy effciency. Smart appliance solutions can cover different aspects of occupants' daily life such as air conditioning, lighting, home security, data privacy, entertainment, surveillance, detection, and assisting living (Wilson et al., 2015).

#### *2.2.3 IoT Architecture*

IoT is one of the most infuencing innovations in the feld of communication (Atzori et al., 2010). Its application in the built environment gives the possibility to make everyday objects intelligent and connected by means of sensing, networking, and processing capabilities (Jia et al., 2019). IoT architectures are generally described and arranged in *Perception, Network and Application* layers. The *Perception Layer* is the physical layer equipped with sensors for sensing and data collection. It detects environmental parameters and identifes other intelligent sensors in the physical space to share information to the upper layers. The *Network Layer*, as the term suggests, is responsible for processing and transmitting the raw data network technologies. The highest level is the *Application Layer*, which

**Fig. 2.1** Three-layers of an IoT architecture

creates the bridge between the building and the end user and supports the decision-making process. Figure 2.1 provides a schematic view of the three-layer IoT architecture.

Nevertheless, IoT needs to use a messaging and connectivity protocol to exchange information from remote locations. The recommended features of such protocol include (a) small code footprint (to be implemented in small devices), (b) low power consumption, (c) low bandwidth consumption, (d) low latency, and (e) use of a publish/subscribe (pub/sub) pattern. The most widespread messaging protocol is Message Queuing Telemetry Transport (MQTT), which is a lightweight pub/sub messaging transcription with a small footprint and minimal bandwidth (Spofford, 2019). Several communication protocols are available for the implementation of IoT architecture; Table 2.4 presents the most common ones.

#### 2.3 Sensing During Construction and Renovation

Information handling is the most important aspect in industrial construction management (CM) (Wang et al., 2007). The main contexts of application in CM involve logistics, cost and time control, real-time process traceability, and operator safety (Ahmad et al., 2016). Recently, building information modelling (BIM) has been largely used in design, construction and facility management processes. The integration of sensors with BIM enables continuous monitoring of building construction stages for accurate construction and renovation management (e.g., cost and time). The integration of sensor data with BIM effectively creates a real-time digital twin that can continuously track changes and any discrepancies during the construction process. This enables the timely remediation of errors and monitoring of the condition of any material on-site by using cameras and other sensors (Liu et al., 2014). Sensor


**Table 2.4** IoT communication protocols

networks are also an effcient solution for supply chain *monitoring* and for managing building materials (Koskela & Vrijhoef, 1999). Table 2.5 the main sensor types that are currently used for mapping buildings for BIM creation and for monitoring construction sites.

BIM-sensor integration plays a key role in *preventive monitoring* during the construction phase to monitor and ensure proper structure conditions (Chen et al., 2020). IoT technologies can be used as a proactive tool to better predict building component failures, unplanned downtime, and broken tools, potentially increasing on-site productivity by up to 25% (Kayar et al., 2021). For automatic retrieval of physical information during the construction process, RFID has been widely adopted (Shen et al., 2010). BIM augmented with sensor data is also crucial for *facility management* (FM). The total cost of ownership of a building is heavily dependent on effective maintenance and the security and safety of the environment for the occupants. Several solutions have been proposed to monitor all building components to prevent failures and malfunctions


**Table 2.5** Sensing construction: mapping sensors

(Cheng et al., 2020; Hemalatha et al., 2017). Finally, sensor networks can be used for monitoring the *end of life phase* at the end of the building life cycle. Tracking systems can be integrated into the building components pre-demolition to ensure traceability and the valorisation of waste (Dave et al., 2016).

#### 2.4 Sensing During Operation: Smart Buildings

Heating, ventilation and air conditioning (HVAC) systems account for about 40% of the energy consumption of a building and therefore their optimisation is critical for SB energy management. The main monitoring functions and the related applied sensors in an SB are presented in Table 2.6.

Recent developments in affordable *IEQ* sensors enable the continuous monitoring of indoor climate and to better analyse building performance. The IEQ monitoring approach consists of deploying many independent environmental sensors to measure air temperature, humidity, carbon dioxide (CO2), particulate matter (PM), air pollutants, illuminance and noise (Choi et al., 2012). Serroni et al. (2021) developed a novel IoT system that includes an IR scanner and environmental sensors for monitoring preand post-renovation building performance. The monitoring of IEQ parameters, based on a non-intrusive IoT systems, allows the detection of building pathologies and such information can be used to support the renovation design. This can ultimately result in better performance


**Fig. 2.2** Concept of the IoT façade module

post-renovation in terms of thermal comfort and indoor air quality. The application of IEQ monitoring to existing buildings can be facilitated with the integration of such sensors with plug and play façade modules for deep renovation. Arnesano et al. (2019), for example, propose the idea of a Smart-IoT façade. The panel is designed to embed sensors to measure indoor and outdoor conditions which are useful for the optimal control of the façade and HVAC (Fig. 2.2).

The advent of new wireless communication technologies and low-cost sensors is opening the possibility for accurate and fne-grained monitoring of the indoor environment in renovated buildings to provide HVAC and lighting systems with optimised control strategies. Kelly et al. (2013) integrated IoT and IEQ sensors in residential buildings implementing the communication between devices using the ZigBee protocol. Parkinson et al. (2019) developed a system consisting of low-cost sensors and a web platform for IEQ rating and analysis. These use cases provide evidence regarding the feasibility of advanced sensing implementation in existing buildings, thereby reducing cost and time for renovation.

#### 2.5 Challenges and Concerns

The application of sensors in the deep renovation context is widely discussed in both research and industry as a way to increase the effciency of the construction sector. However, managing the massive amount of data generated by both buildings and occupants creates a series of challenges and concerns that researchers and practitioners need to address.

Cloud computing has been defned as the main widespread method for sensors' data management (Mell & Grance, 2011). As cloud service providers (CSPs) typically operate using a distributed model, data can be subject to different jurisdictions. Thus, the choice of law can favour the CSP or the end user (Lynn et al., 2021). The service-level agreement (SLA) is the contract that defnes the CSP's assurances on availability, reliability and performance levels for cloud service. In general, CSPs tend to minimise their liability for any loss and attempt to compensate for those issues through service credits (Bachmann et al., 2015). The rules for data management are defned by the acceptable use policy (AUP), which defnes the prohibited activities and behaviours of the end users. It is important that AUP is aligned from both sides, CSP and clients, to avoid some issues (Hon et al., 2012).

Typically, CSPs are the "data processors" but also the "data controllers". The EU General Data Protection Regulation (GDPR) defnes the data protection and privacy policies in case of accidental destruction, loss, or unauthorised disclosure of or access to personal data, without guaranteeing the integrity and availability of all data (Lynn et al., 2021). At the termination of the contract between a CPS and a client, an adequate provision for the subsequent handling of the client data needs to be provided (Bradshaw et al., 2011). In addition to the cloud-related data protection issues, challenges related to sensor data for automation in construction still represents a signifcant barrier mostly due to (1) a general lack of maturity in the use of information, (2) low level of investment in sensor technology and (3) diffculties in implementing effective communication and collaboration between stakeholders (Chen et al., 2018). In addition, industry is concerned about the lack of standardised practices for cost estimation and information security.

Considering the human side of the sensors, technologies for the end user should be capable of sensing environmental and personal contexts to ensure functional reliability. In smart homes where occupants need special assistance, failures or inaccurate inferences about the occupants' behaviour can lead to life-threatening consequences (Orpwood et al., 2005). There are signifcant concerns relating to privacy and security. For example, privacy could be compromised if data from IEQ sensors could provide information on the occupancy of the workplace (Cascone et al., 2017) or of the smart home (Cook, 2012). With regard to security, sensor data needs to be covered by legal protection, in case of malicious or unintentional data exposure (Sicari et al., 2015). Thus, when sensing technologies are developed, adequate consideration of the consequences of the data generated and their fnal use must be considered.

#### 2.6 Conclusion

This chapter sheds light on sensing networks and their application for deep renovation, as well as considering the ethical and legal implications of their use. The introduction of sensing technologies presents opportunities to optimise and manage the construction and renovation process, from production to the end of life of buildings, and ideally both reduce costs and energy effciency in existing buildings. Nevertheless, the huge amount of available data monitored represents a signifcant risk to data privacy and security. Those working with sensors and sensor data need to be knowledgeable about cybersecurity risks and appropriate mitigation measures.

#### References


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Building Information Modelling

*Omar Doukari, Mohamad Kassem, and David Greenwood*

**Abstract** From its origins as a computer-aided three-dimensional modelling tool, Building Information Modelling (BIM) has evolved to incorporate time scheduling, cost management, and ultimately an information management framework that has the potential to enhance decision-making throughout the whole life-cycle of built assets. This chapter summarises state-of-the-art BIM and its benefts. It then considers the particular characteristics of deep renovation projects, the challenges confronting their delivery, and the potential for using BIM to meet the challenges. This includes the application of Artifcial Intelligence (AI) and Machine Learning (ML) to BIM models to optimise deep renovation project delivery. The prospects for this are encouraging, but further development work, including the creation of ontologies that are appropriate for renovation work, is still needed.

e-mail: omar.doukari@northumbria.ac.uk; david.greenwood@northumbria.ac.uk

M. Kassem

School of Engineering, Newcastle University, Newcastle upon Tyne, UK e-mail: mohamad.kassem@newcastle.ac.uk

O. Doukari (\*) • D. Greenwood

Department of Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne, UK

<sup>©</sup> The Author(s) 2023

T. Lynn et al. (eds.), *Disrupting Buildings*, Palgrave Studies in Digital Business & Enabling Technologies, https://doi.org/10.1007/978-3-031-32309-6\_3

**Keywords** BIM applications • 4D BIM • BIM dimensions • Deep renovation projects

#### 3.1 Introduction

'BIM' can refer to an item (i.e., a building information model) as in its description by the US National Institute of Building Sciences as 'a digital representation of the physical and functional characteristics of a facility' (National Institute of Building Sciences, 2021). BIM can also be the process of managing construction information. This is defned by Hamil (2022) as '[…] creating and managing the information on a project throughout its whole life cycle'. Succar and Kassem (2015) have observed that BIM is a byword for digital innovation in construction. The concept of BIM frst emerged in the early 1990s when earlier Computer-Aided Design (CAD) and 3D CAD software systems evolved into object-oriented 3D design tools containing geometric as well as non-graphical data. The term frst became used in the early 2000s, notably in a white paper by Autodesk (2002). Nearly two decades after its frst coinage as 'BIM', it has become a framework for managing information across the whole life cycle of projects as evidenced by the ISO 19650-1:2018 and ISO 19650-2:2018 standards and other related standards and guidance.

However, BIM has not permeated every part of the industry (Hamil & Bain, 2021) and there has been a temptation to 'cherry pick' convenient elements of the technology, leaving many wider aspects of BIM overlooked and their benefts unexploited (Georgiadou, 2019). There is also uncertainty over what BIM adoption actually means. Industry surveys predominantly refect the use of BIM software, while academic studies tend to elicit the opinions of individual survey respondents. This has prompted attempts to measure BIM maturity. These range from the early Bew-Richards model comprising four levels of BIM (BSI, 2013) to more detailed multi-component approaches initiated by Succar (2009), further developed by Succar and Kassem (2015) to measure the maturity of countries or markets. Despite all the efforts, barriers to BIM adoption still remain. Begić and Galić (2021) found the most prominent of them to be resistance to change, required investment in software and skills, and cybersecurity concerns.

The remainder of this chapter is structured as follows. Section 3.2 explores BIM's benefts by examining its various applications through the project life cycle. The problems of delivering deep renovation projects are reviewed in Sect. 3.3, and then Sect. 3.4 considers how BIM can offer solutions. Section 3.5 considers some of the remaining challenges, and Sect. 3.6 offers a perspective on how current and future developments in BIM can overcome these challenges. Finally, Sect. 3.7 presents some concluding remarks.

#### 3.2 BIM Applications, Benefits, and Beyond

BIM originated as an enhanced 3D design tool but soon began to offer a wider range of functional applications that extended its range of use cases: so-called BIM dimensions. These offer the prospect of a unifed model that can enable the effcient and effective sharing of data between different functions and throughout the project life cycle.

Table 3.1 presents a (non-exhaustive) list of the commonly recognised BIM dimension. In reality, once they go beyond 4D BIM, where a time schedule is linked to a 3D physical BIM model, these so-called dimensions


**Table 3.1** Commonly recognised BIM 'dimensions'

Note: Further uses (safety, accessibility, security) have been proposed, with no consensus on terminology (Charef et al., 2018). The term 'nD model' refects the range of possibilities (Aouad et al., 2006)


**Table 3.2** Metaphoric 'dimensions' debated in literature

are simply applications. As Koutamanis (2020) points out, time can realistically be considered a dimension, whereas cost (5D), sustainability (6D), or life cycle (7D) are metaphors. The terminology is nevertheless retained here as it is still widely recognised. Stepping aside from the BIM dimensions, a more inclusive coverage of the applications and uses of BIM is represented by the PennState BIM uses (2023) or the concept of model uses of Succar et al. (2016) (Table 3.2).

Related literature suggests that BIM can generate a number of organisational benefts. According to Georgiadou (2019), they include design optimisation, improved on-time delivery, cost effciency, quality assurance, collaboration and communication, and sustainability. Ghaffarianhoseini et al. (2017) also add technical superiority, interoperability, information capture, improved cost control, whole-life applicability, the potential for integrated procurement, and reduced confict and better communication and coordination within the project delivery team. Attempts to quantify the value of such benefts using, for example, a return on investment (ROI) approach are necessarily contextspecifc. This is confrmed by Sompolgrunk et al. (2021) that found a huge variance in reported ROIs. Positive results were predominantly associated with schedule reduction/compliance, increased productivity, and reduction in requests for information, change orders, and rework.

As highlighted in Begić and Galić (2021), BIM is a vital element in the transformation to 'Construction 4.0', where innovations such as the Internet of Things (IoT), blockchain, and artifcial intelligence (AI) and modern methods of construction (MMC) will play an increasing role in the built environment, and built assets will have a golden thread of information showing how they have been built and how they are performing (Hamil, 2022).

The digital and object-oriented basis of BIM allows it to interact with other digitally driven systems which can represent either inputs to a BIM model (e.g., the retrospective modelling of existing facilities through point-cloud surveys) or outputs (e.g., the automated manufacture of building components from their design). Other examples include the integration of BIM with blockchain to overcome challenges related to provenance, accuracy, transparency, security, and ownership of model information (Li et al., 2019). Furthermore, an opportunity for transforming the management of built assets comes with the concept of the 'digital twin'—a cyber-physical system where live data fows from sensors1 in the physical asset (e.g., a building) into its counterpart digital model (De Luca et al., 2021). Conversely, the physical twin can be controlled from the model to enable operation, maintenance, monitoring, diagnostics, prediction, and simulation. These activities can focus on such important issues as energy use, carbon emissions, and planned maintenance.

#### 3.3 Deep Renovation Projects: Key Challenges

The delivery of construction projects in general can be complex and demanding and presents well-documented challenges to the control of cost, schedule, and quality. This situation becomes even more acute in the case of renovation projects, which are inherently more uncertain.

Planning and execution of deep renovation2 projects are currently driven by judgement and experience rather than standardised solutions (Amorocho & Hartmann, 2021; Lynn et al., 2021). Such projects typically disturb existing building occupants, whose presence, conversely, disrupts construction logistics, schedules, and budgets. Deep renovation projects, which aim at maximising energy effciency in the renovation process (Shnapp et al., 2013) are even more problematic because of their extended impact on the fabric, services, and even structure of a building (Fawcett, 2014). McKim et al. (2000) reported that renovation projects were twice as susceptible to delay and suffered four times the cost overruns of new construction work. Their conclusion was that conventional

<sup>1</sup>Chapter 2 in this book provides more details on the use of sensor networks in the context of deep renovation projects.

<sup>2</sup>Chapter 1 in this book provides a detailed defnition of Deep Renovation.

time and cost control techniques were inadequate for such projects. Alongside the uncertainties surrounding the work itself is the safety and well-being of building occupants. Chaves et al. (2016) have highlighted disruptions involving: (a) utilities (gas, electricity, telecoms); (b) access; (c) use of space by both occupants and contractors; (d) problems with internal environmental quality (noise, dust, vibration, and debris); (e) external environmental quality; and (f) transport and parking spaces. To mitigate such issues and allow project teams to plan, organise, and effciently realise renovation tasks, Killip et al. (2013) have suggested the adoption of new technologies and optimised processes: approaches that are epitomised by BIM-based applications. BIM benefts are much reported in literature but rarely in relation to renovation projects.

# 3.4 The Potential for BIM in Deep Renovation Projects

In their state-of-the-art review of design decision-making for sustainable renovation projects, Passoni et al. (2021) stress the need for multi-criteria decision methods, optioneering, and pre-validation of proposals. Although their work relates to the *design* of sustainable renovation projects, the conclusions apply equally to their delivery. In both cases digital tools based on BIM can be employed to identify, optimise, validate, and communicate different renovation scenarios, in terms of cost, time, and effectiveness in meeting the required functionality and quality of the resulting work.

A foundation for using BIM in most renovation projects is employing a laser scanner to capture point-cloud data that can then be processed to create a 3D BIM model (Wang & Kim, 2019). The retro-constructed geometric 3D BIM model can then be semantically enriched to enable further functionalities. Thus the 'scan to BIM' or 'mapping' stage can provide the basis for the application of BIM in renovation projects as described by D'Oca et al. (2018) in their review of related European Horizon 2020 projects. The captured as-built BIM models can be used for building condition assessment that can underpin the prioritisation of renovation options. For example, Sebastian et al. (2018) describe the use, based on the initial scanned model, of software applications for assessing conditions and analysing and prioritising renovation interventions on the basis of their energy performance. Acampa et al. (2021) have also shown how BIM-based decision support systems have been used to generate such optimal renovation scenarios.

BIM enables a common data environment (CDE) for information exchange between the various design consultants, connecting energy simulation and prediction (Garwood et al., 2018; Pinheiro et al., 2018), life cycle costing (Edwards et al., 2019; Sharif & Hammad, 2019) and its parametric modelling capacity enables quicker and more cost-effective design optimisations (Abanda & Byers, 2016; Corgnati et al., 2017).

From a project delivery perspective, BIM can enhance the management of project schedules through 4D BIM (Jupp, 2017; Sheikhkhoshkar et al., 2019) and budgetary control using '5D BIM' (Lee et al., 2016). The benefts of doing so include more critical assessment of options, more effective coordination and work sequencing, improved tracking and review, enhanced utilisation of space and resources, control of waste, and improved communication (Gledson & Greenwood, 2017). In deep renovation projects, these benefts are amplifed. In fact, the uncertainty inherent in renovation work requires fexible approaches, and in this respect, the ability of BIM-based simulations of time and cost to evaluate different renovation scenarios offers great potential (Chaves et al., 2016).

Finally, from a communication perspective, the use of BIM simulation and visualisation can be useful in mitigating disruption to (and by) occupants (Passoni et al., 2021). Crucially, BIM simulations of time and cost can enable users to share and clarify the perception of the renovation process with all stakeholders, including building occupants who are unlikely to fully understand traditional drawings and schedules. The ability to use visualisation to demonstrate design and construction decisions and their consequences in time and space, including any different options that are available, can clearly facilitate good relations and better cooperation with owners and occupiers. This, in turn, should assist the renovation process.

## 3.5 Immediate Challenges in the Adoption of BIM Solutions

The prospects for the use of BIM in deep renovation projects are encouraging, but there remain challenges, some of which are related to interoperability and workfow. As observed by De Gaetani et al. (2020), the multidisciplinary nature of construction design attracts the use of different types of authoring software, fle formats, or (even if formats are the same) different fle format versions. Thus, additional effort may be required for fle exchange. The development of time- and cost-related models from initial 3D design models is typically performed later in the project process by the contractor. Here the inherent advantage of BIM is the opportunity to extract objects from the design model to generate scheduled activities and budget items. As identifed by Park and Cai (2015), this process involves four phases:


In theory, this process could be automated (ElMenshawy & Marzouk, 2021), but this depends on the interoperability between the applications used and the degree of coordination with the original design. Designoriented 3D BIM models are rarely set up to facilitate the subsequent production of schedule and budgetary tools. As a result, signifcant manual effort, involving the splitting or aggregating of objects, is required to link the elements of the 3D model to the relevant time and cost parameters. Such interoperability and workfow challenges must be overcome to unlock the full effciencies of information transfer enabled by BIM adoption.

# 3.6 Further Developments and Challenges

Furthermore, the availability of BIM models for delivering renovation projects presents an opportunity to exploit advances in Artifcial Intelligence (AI) and Machine Learning (ML). Mulero-Palencia et al. (2021) raise the possibility of applying AI and ML to BIM models of deep renovation interventions. Their focus is on the development of algorithms for diagnosis (at the preliminary analysis stage of a project) and optimisation (at the design simulation and optioneering stage), but there are implications for all stages in the life cycle of deep renovation projects.

A current barrier to doing so is the lack of ontologies that are appropriate for renovation work. Ontologies are fundamental requirements for formalising specifc domain knowledge including concepts, relations, and constraints and are thus an essential basis for producing machine-readable code that can support process automation (Hartmann & Trappey, 2020). As noted by Amorocho and Hartmann (2021), BIM-based tools for design, planning, and project management are normally targeted at new construction and comprehensive ontologies for renovation activities are not currently available. Amorocho and Hartmann (2021) have developed a limited example ontology that was restricted to the installation of common renovation products, such as windows, HVAC components, and external thermal insulation panels. However, no ontology currently exists for the general case of renovation projects, and further development will be necessary to capture the potential of BIM-driven AI solutions.

#### 3.7 Conclusion

This chapter summarises the state-of-the-art on BIM with a specifc emphasis on its potential in deep renovation projects. In the course of domestic renovation works, disruptions to users and occupants are inevitable. This and the uncertainties inherent in this type of work make the delivery of such projects challenging—particularly in adhering to time and cost plans. Deep renovations are especially problematic in this respect. The use of BIM would not only enable the integration of condition assessments with subsequent building design, but also permit the automatic extraction of design information to generate schedules and budgets and to control them. Based on BIM, other technologies, such as AI and ML, could be applied to generate standardised and optimised solutions for the delivery of deep building renovation projects.

#### References


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Building Performance Simulation

# *Asimina Dimara, Stelios Krinidis, Dimosthenis Ioannidis, and Dimitrios Tzovaras*

**Abstract** Simulation is a proven technique that uses computational, mathematical, and machine learning models to represent the physical characteristics, expected or actual operation, and control strategies of a building and its energy systems. Simulations can be used in a number of tasks along the deep renovation life cycle, including: (a) integrating simulations with other knowledge-based systems to support decision-making, (b) using simulations to evaluate and compare design scenarios, (c) integrating simulations with real-time monitoring and diagnostic systems for building energy management and control, (d) integrating multiple

A. Dimara • D. Ioannidis • D. Tzovaras

Information Technologies Institute, Centre of Research & Technology Hellas (CERTH), Thessaloniki, Greece

e-mail: adimara@iti.gr; djoannid@iti.gr; Dimitrios.Tzovaras@iti.gr

S. Krinidis (\*)

Information Technologies Institute, Centre of Research & Technology Hellas (CERTH), Thessaloniki, Greece

Department of Management Science and Technology, International Hellenic University, Kavala, Greece e-mail: krinidis@iti.gr

T. Lynn et al. (eds.), *Disrupting Buildings*, Palgrave Studies in Digital Business & Enabling Technologies, https://doi.org/10.1007/978-3-031-32309-6\_4

simulation applications, and (e) using virtual reality (VR) to enable digital building design and operation experiences. While building performance simulation is relatively well established, there are numerous challenges to applying it across the renovation life cycle, including data integration from fragmented building systems, and modelling human-building interactions, amongst others. This chapter defnes the building performance simulation domain outlining signifcant use cases, widely used simulation tools, and the challenges for implementation.

**Keywords** Building simulation • Building performance simulation • Building simulation applications

### 4.1 Introduction

Building simulation (BS) is the process of creating a digital replica of a building, while building performance simulation (BPS) is a model that evaluates how the building performs under real-life conditions (Mahdavi, 2020). During the replication process, digital copies are created of the whole building—its exterior and interior, and, in some cases, the building's distinct parts (e.g., apartments and rooms) if needed. The BPS process consists of fve main phases as depicted in Fig. 4.1. The main objective of this process is to defne the best performance criteria and the most suitable actions by applying performance simulations. Once results are generated, they are evaluated against initial expectations and requirements.

Mathematical and intelligent models and applications are exploited to recreate (simulate) various external and internal conditions while representing them in a virtual environment (Mahdavi, 2020). BPS makes it easier for different stakeholders (building managers, architects, engineers, etc.) to inspect and check salient points, elements, and other aspects of the building's life cycle (i.e., early design, construction, retroftting, monitoring, inspection, and demolition) (Bramstoft et al., 2018).

Exploiting BS tools and applications is faster, safer, and less expensive than producing a real use case scenario. It supports product and system testing without having to build them in real life and is often less time-consuming and costly while also being safer. Moreover, BPS may be exploited for identifying building problems by replicating and producing different conditions. Finally, it may be used to model specifc changes to check how the building reacts in the short or the long term (Fernandez-Antolin et al., 2022).

The remainder of the chapter is structured as follows: Sect. 4.2 describes the main applications of and approaches to BPS. Section 4.3

**Fig. 4.1** Building performance simulation process (Bramstoft et al., 2018)

introduces BPS in more detail. Finally, Sect. 4.4 presents some concluding remarks.

# 4.2 Building Performance Simulation Approaches and Applications

BPS is a dynamic technique that is used to predict the behaviour of a building while optimising energy effciency (Attia et al., 2013). The key objective of BPS is to reduce the building's environmental footprint while improving indoor environmental quality (IEQ). At the same time, if applied correctly, BPS may facilitate technological innovation and progress in building construction (Loonen et al., 2017). BPS energy models are applied in a number of real-life applications. These include, for example, energy conservation, energy monitoring, energy savings, and fault detection. Simulations may include load and energy simulation, energy management simulation, virtual reality simulations, and a wide range of other simulations based on stakeholders' needs (Martins, 2022).

### *4.2.1 Integrating Simulations with Other Knowledge-Based Decision Support Systems*

Knowledge-based systems employ various and numerous techniques such as statistical analysis, artifcial intelligence methods, knowledge and data visualisation, engineering, and other methods (Alor-Hernández & Valencia-García, 2017). These techniques have been developed to be integrated into heterogeneous systems including decision support systems (DSS), software agents (SAs), and knowledge engineering (KE) which may be strongly related to BS as described in Table 4.1. All these systems use prior knowledge to exclude, determine, and propose further knowledge. To deploy a knowledge-based system, an analysis of the building's energy conservation is needed to inspect the building's energy effciency.


**Table 4.1** Knowledge-based systems and exemplar simulation use cases

In general, knowledge-based systems are the main component of building simulation and are mainly used as the process that emulates the simulation outcome.

#### *4.2.2 Using Simulations to Evaluate and Compare Design Scenarios*

Simulations provide many advantages compared to a conceptual design. For example, BPS provides the ability to examine various solutions during the design stage like the effciency of the building's equipment and integration, therefore reducing development time and energy consumption and emissions. Moreover, BPS may facilitate the optimisation of thermal and visual comfort by simulating the building's fenestration and massing. This process may also be deployed in the early stages while designing the façade. Finally, BPS enables the simulation of heating, ventilation, and air conditioning (HVAC) systems to defne the optimal setup.

#### *4.2.3 Integrating Simulations with Real-Time Monitoring and Diagnostic Systems for Building Energy Management and Control*

Radiation, conduction, and convection are mass and heat transfer phenomena that take place in a building and are the key inputs for energy and load simulations (Yu, 2019). As a result, every BS needs to take into account different mechanisms behind such phenomena. Heat and mass transfer are carried in the building by air movements and are embodied by the indoor air pressure stack between outdoor and indoor places (Vera, 2018). Those phenomena are infuenced by peoples' activities, heating and cooling systems, and ventilation as well as building insulation and orientation (Puttur et al., 2022). Therefore, thermal and airfow models are applied to represent heat and mass transfers of buildings as they are signifcantly associated with the energy transfer (Tian, 2018b) and are used to calculate loads and simulate energy consumption (Tan et al., 2022).

Frequently used models for simulating energy consumption include Computational Fluid Dynamics (CFD) models, zonal models, and multizone models (Laghmich et al., 2022). CFD models separate the building into cells to simulate load and energy consumption (Shen et al., 2020). A multi-zone model uses rooms as computational elements for the simulation, while a zonal model uses several zones by separating a room into


**Table 4.2** Airfow models for energy simulation comparison (Laghmich et al., 2022)

smaller units (Yu, 2019). An overall assessment of the aforementioned models is presented in Table 4.2.

Other models estimate a building's energy consumption by exploiting physical models (Kampelis et al., 2020). These models simulate energy consumption by exploiting mathematical equations and the building's energy conservation. While such mathematical or computational models are suffciently precise, they require holistic building information (Oucquier et al., 2013). Furthermore, a unique model is required for each building. Another widely referenced approach to simulating building energy load is data-driven modelling (Bermeo-Ayerbe et al., 2022). These models use indoor monitoring and measurements (e.g., relative humidity, temperature, historical consumption data, historical load, and generations data) to predict energy consumption (Kampelis et al., 2020). A benchmarking analysis of various regression models using energy consumption suggests that, in many cases, these models are suffciently accurate for building simulation and may be used as a generic solution (Dimara et al., 2021).

To manage the overall energy consumption of a building, various load controls are applied to manage both energy savings and comfort regulation. The energy load of all appliances in a building are simulated in order to build predictive models for energy consumption and deploy an accurate energy management strategy (Fanti et al., 2018). The main problem when trying to fnd optimal control states is to detect the best strategies for heating, cooling, ventilation, and lighting that result in energy savings while maintaining desirable indoor conditions for the occupants. As such, all possible energy load actions must be simulated accurately.

Building energy modelling and simulation allows stakeholders to better understand certain energy operating characteristics before designing, applying, or testing them in a real-life scenario. Furthermore, it helps with reducing waste and allows energy-saving verifcation by testing real data against various scenarios which may take into account multiple factors such as weather conditions and occupancy patterns.

#### *4.2.4 Integrating Multiple Simulation Applications*

As mentioned previously, most simulations require the deployment of multiple models, applications, and techniques to provide an overall building assessment. In general, deploying and integrating automated multisimulation applications may produce signifcant advantages when compared to a single simulation. During this procedure, a couple of models are integrated and their output is combined. For example, a comprehensive evaluation of the HVAC systems in a building would require the combination of airfow, heat losses, atmospheric conditions, and energy performance simulations.

# 4.3 Building Performance Simulation Use Cases

Deployment of modelling and simulation tools for building performance can be implemented in various and numerous use cases from the design stage to operation and management of a building. Some of the most common BPS simulation use cases are summarised in Table 4.3.

To implement any of the use cases above or any type of building simulation, appropriate tools and technologies must be applied. Some indicative technologies and commercial tools for BPS are summarised in Tables 4.4 and 4.5.

# 4.4 Building Simulation: Challenges and Concerns

In the summary of literature on BPS, Attia (2010) identifes fve major challenges—(a) interface usability and information management, (b) integration of decision design support and design optimisation, (c) accuracy and ability to simulate detailed and complex building components, (d) integration with other tools in the building design and construction/ renovation process, and (e) BIM1 integration and interoperability. These

<sup>1</sup>Chapter 3 in this book provides a detailed discussion on BIM.




**Table 4.4** Indicative simulation technologies (Khajavi et al., 2019)

a Chapter 6 in this book provides a detailed discussion on digital twins


**Table 4.5** Commercial simulation tools

challenges were echoed and expanded more recently by Hong et al. (2018). The ten challenges identifed by Hong et al. (2018) cover the full building life cycle and have been updated to include zero-net-energy (ZNE) and grid-responsive buildings, as well as urban-scale building energy modelling (see Table 4.6).


**Table 4.6** Ten challenges of building performance simulation

(*continued*)


**Table 4.6** (continued)

Adapted from Hong et al. (2018)

While the BPS challenges Attia (2010) and Hong et al. (2018) present at a high level are not insignifcant, at a more granular and operational level, the selection of a BPS tool is also not without challenges. In addition to the level of accuracy and detail, usability and information management, data exchange capacity, database support, interoperability with building modelling, and integration of building design process, Solmaz (2019) highlights the speed (time to implement), cost, and ease of use of BPS tools as signifcant issues. Given the centrality of BIM in the building and deep renovation2 process, it is important to highlight specifc challenges noted by many studies with respect to the integration of simulations and specifcally energy simulations in BIM (Østergård et al., 2016; Hong et al., 2018; Kamel & Memari, 2019; Solmaz, 2019). Challenges include interactions between components, fle-related interoperability issues at the fle and syntax level, visualisation level, and semantic level, different calculation methods, attribute support, missing data, and data loss between systems (Kamel & Memari, 2019).

### 4.5 Conclusion

Building simulation can provide valuable insights across all the stages of the building and deep renovation life cycle. It may be used for many use cases as it is adaptable to various inputs and it can be deployed based on different needs. Furthermore, there is a plethora of tools and technologies that may support the simulation process. Nevertheless, simulation technology is evolving rapidly with advancements in simulation techniques, software, and hardware. Moreover, most of the simulation applications or auxiliary tools (e.g., BIM and DT) have signifcant data demands, leading to higher demand for storage and computational resources. As a result, new big data handling solutions must be developed to support the simulation process. In the future, building simulation will undoubtedly be a signifcant element in the whole cycle of the building; however, both integration and interoperability remain signifcant challenges that should not be underestimated.

#### References

*acousticcalc*. (2022). http://www.acousticcalc.com/


<sup>2</sup>Chapter 1 in this book provides a detailed defnition of deep renovation.

Bramstoft, R., Alonso, A. P., Karlsson, K., Kofoed-Wiuff, A., & Münster, M. (2018). STREAM—An energy scenario modelling tool. *Energy Strategy Reviews, 21*, 62–70.

*buildingos*. (2022). https://atrius.com/welcome-buildingos/

*buildsim*. (2022). https://www.buildsim.io/

Dimara, A., Anagnostopoulos, C. N., Krinidis, S., & Tzovaras, D. (2021, January). Benchmarking of regression algorithms for simulating the building's energy. *In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference* (CCWC) (pp. 94–100). IEEE.

EnergyPlus. (2022). https://energyplus.net/.


*GBS*. (2022). https://gbs.autodesk.com/GBS/


*IDA*. (2022). https://www.equa.se/en/ida-ice

IESVE. (2022). https://www.iesve.com/


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Big Data and Analytics in the Deep Renovation Life Cycle

*Paraskevas Koukaras, Stelios Krinidis, Dimosthenis Ioannidis, Christos Tjortjis, and Dimitrios Tzovaras*

**Abstract** The rising volume of heterogeneous data accessible at various phases of the construction process has had a signifcant impact on the construction industry. The availability of data is especially advantageous in the context of deep renovation, where it may signifcantly accelerate the decision-making process for building stock retroft. This chapter covers Big Data and analytics in the context of deep renovation and shows how Machine Learning and Artifcial Intelligence have affected the various phases of the deep renovation life cycle. It presents a review of the literature on Big Data and deep renovation and discusses a series of use cases,

P. Koukaras (\*) • C. Tjortjis

Information Technologies Institute, Centre for Research & Technology Hellas (CERTH), Thessaloniki, Greece

School of Science and Technology, International Hellenic University, Thessaloniki, Greece e-mail: p.koukaras@iti.gr; c.tjortjis@iti.gr

<sup>©</sup> The Author(s) 2023

T. Lynn et al. (eds.), *Disrupting Buildings*, Palgrave Studies in Digital Business & Enabling Technologies, https://doi.org/10.1007/978-3-031-32309-6\_5

applications, advantages, and benefts as well as challenges and barriers. Finally, Big Data and deep renovation prospects are discussed, including future potential developments and guidelines.

**Keywords** Big Data • Deep Renovation • Artifcial Intelligence • Machine Learning • Energy

# 5.1 Introduction

As in many industries, the construction sector has been impacted and somewhat changed by the growing volume of heterogeneous data available at different stages of the construction process. This trend is expected to continue as technologies such as sensors and the Internet of Things (IoT) become more and more accessible and commoditised. The availability of data is particularly useful in the context of Deep Renovation (DR)1 where it can dramatically accelerate the decision-making for building stock retroft. This chapter defnes Big Data (BD) and analytics in the context of DR and describes how the use of BD and advanced analytics such as Machine Learning (ML) and Artifcial Intelligence (AI) may impact different stages of the DR life cycle.

The remainder of this chapter is organised as follows. Section 5.2 provides an overview of BD and different types of analytics. Section 5.3 presents a series of use cases and applications of BD in construction. Section 5.4 describes how BD can be used in the DR space. Section 5.5

1Chapter 1 in this book provides a detailed defnition of deep renovation.

S. Krinidis

Department of Management Science and Technology, International Hellenic University, Kavala, Greece e-mail: krinidis@mst.ihu.gr

D. Ioannidis • D. Tzovaras

e-mail: djoannid@iti.gr; Dimitrios.Tzovaras@iti.gr

Information Technologies Institute, Centre for Research & Technology Hellas (CERTH), Thessaloniki, Greece

Information Technologies Institute, Centre for Research & Technology Hellas (CERTH), Thessaloniki, Greece

discusses the advantages and benefts of BD in the context of DR. Section 5.6 outlines challenges and barriers related to the adoption and use of BD in construction generally and in DR more specifcally. Section 5.7 presents potential future developments and, fnally, Sect. 5.8 contains some concluding remarks.

#### 5.2 Big Data Analytics

BD analytics deals with large, heterogeneous data sets from various sources. Data-driven decision-making entails fnding trends, patterns, and correlations in data. In order to do that, different types of BD analytics can be implemented which can be classifed under four main categories that is, descriptive, diagnostic, predictive, and prescriptive analytics. Data mining, cleansing, integration, and visualisation enable data analytics in various domains and change and/or improve DR processes, delivering commercial and societal benefts (Rajaraman, 2016; Koukaras & Tjortjis, 2019; Kousis & Tjortjis, 2021).

*Descriptive analytics* is a popular way for organisations to analyse current and past trends and operational performance. It is the initial stage in interpreting raw data by applying relatively basic statistics and creating sample and measurement statements.

*Diagnostic analytics* is a type of BD analytics used to evaluate data and content. This form of analytics typically answers questions like 'why did something happen?' and therefore aims to explain the causes behind particular results.

*Predictive analytics* involves estimating outcomes using data insights. It typically employs ML and statistical modelling to predict the most likely outcomes.

*Prescriptive analytics* is built on the insights gained from descriptive, diagnostic, and predictive analytics to optimise operational processes using simulations and related tools (see Chap. 4 for more details). It uses statistics and data modelling to assist organisations understand and predict the market or environment. It helps individuals defne priorities and recognise what might lead to fnancial or other types of rewards.

# 5.3 Use Cases and Applications of Big Data in Construction

BD analytics is backed by BD engineering, which has signifcant construction applications. BD engineering involves Building Information Modelling (BIM)2 to enhance project management (Huang, 2021), building design and monitoring performance (Loyola, 2018), safety, energy management, decision-making design frameworks, resource management (Ismail et al., 2018), quality management, waste management (Wang et al., 2018), and more (see Chap. 3 for a more detailed discussion). Moreover, BD platforms that perform BD analytics in construction are essential to BD engineering and may be classifed as Horizontally Scaling Platforms (HSPs) and Vertically Scaling Platforms (VSPs). HSPs use several servers by spreading processes and adding additional devices, while VSPs scale by updating the server's hardware. Waste management (Bilal et al., 2016b), proftability performance measurement (Bilal et al., 2019), smart road construction, and others (Sharif et al., 2017) typically employ HSPs, while VSPs have been mostly used in construction (Curtis, 2020) and transportation (Shtern et al., 2014).

Furthermore, deep learning–based food detection and damage assessment (Munawar et al., 2021), project delay risk prediction (Gondia et al., 2020), construction site safety (Tixier et al., 2016), construction site monitoring (Rahimian et al., 2020), and neural network models to predict concrete qualities (Maqsoom et al., 2021) are a few instances of AI and ML in construction.

# 5.4 Big Data and Deep Renovation

The fundamental components of BD engineering include both distributed and parallel processing. BD analytics has been used in the construction industry for a variety of purposes, including waste management (Lu et al., 2016), management of prefabricated building projects (Han & Wang, 2017), proftability studies, and other construction management applications (Bilal et al., 2019).

BD in construction uses AI and ML for revitalising sustainable architecture, energy-effcient building design, and minimising environmental and climatic consequences. Recent advancements in

<sup>2</sup>Chapter 3 in this book provides a detailed discussion on BIM.

internet speed, accessibility, processing cost, and data storage cost make BD a vital AI supplement (Mehmood et al., 2019).

In recent years, AI has contributed signifcantly to improving learningbased decision-making. Its use in building design and engineering along with BIM is offering new options for DR utilising BD since very large volumes of construction-related data are available. DR is one of the main drivers for Greenhouse Gas (GHG) emission reduction in cities (Avramidou & Tjortjis, 2021) and along with ML and AI introduces new design potentials, constraints, and solutions. Overall, BIM and Industry Foundation Classes (IFC) improve DR's decision-making and the energy effciency of retroftted buildings (Mulero-Palencia et al., 2021).

Nowadays, DR should aim to harness the maximum economic energy effciency potential of construction activities at a large scale, thus utilising BD for construction purposes. It should also concentrate on improvements of the building shell of existing structures, leading to extremely high-energy performance. Nonetheless, residential effciency improvements or criteria (e.g. shell upgrades or Heating, Ventilation and Air Conditioning (HVAC) and hot water system upgrades) vary by climate (Cluett & Amann, 2014).

Despite the European Union's (EU) energy effciency targets and renovation actions such as aesthetic improvement of the building outer façade, increased thermal comfort and energy effciency, and CO2 emission minimisation, the construction industry has not yet adopted large-scale standardised retroftting techniques that would involve BD analytics in construction (Glumac et al., 2013). Most renovation options include external/internal insulation, air tightening the transparent and opaque building envelope, roof conversion, solar panels, heat recovery, and more effcient HVAC systems. Conventional energy retrofts focus on single system upgrades, such as façade, lighting, and HVAC equipment, without considering integrated renovation options.

#### 5.5 Advantages and Benefits

Literature suggests a number of opportunities for BD adaptation in the DR context (Bilal et al., 2016a):

1. *Generative design*. The idea is to automate the development of several design models based on specifc objectives such as functional requirements, material type, manufacturing process, performance standards, and cost limitation. Such tools use advanced algorithms to develop design solutions that fulfl design criteria. Designers evaluate the designs' performance and are able to change design objectives and restrictions until they are satisfed.


6. *Personalised services*. Such services emphasise on adapting facilities to user preferences. Users control how services are utilised and these systems adapt to user behaviour. They consider both human and automated input. Therefore, personalisation solutions monitor the surroundings for occurrences of interest, creating vast amounts of data. BD technologies can analyse these data streams in real-time to create actionable insights for nearly instant adaption. To do so, contemporary buildings need BD-enabled platforms with a uniform interface to facilitate such personalisation services.

Other advantages/benefts of BD and analytics in construction include:


# 5.6 Challenges and Barriers

In recent years, energy effciency has become one of the EU's top priorities (Koukaras et al., 2021a). Some generic barriers of BD in DR are (Lynn et al., 2021) as follows:

1. *Human*. Several variables may impede the approval, support, and adoption of energy-effcient behaviours, technologies, and initiatives. Social norms, behavioural patterns, inability to use new technologies, lack of information on energy consumption and energy-saving opportunities, and more are all barriers. Moreover, education, age, and family composition affect the adoption of energy-effcient equipment. All these underscore the necessity of adjusting communication to various groups and educating construction experts for adopting and using BD analytics for analysing these data to elevate DR.


More barriers related specifcally to data, that is, BD, applicable in the context of DR are (Bilal et al., 2016a): (a) data security, privacy, and protection, (b) data quality of construction industry data sets, and (c) fast and reliable internet connectivity for BD applications.

In addition, other challenges related to BD handling in construction are (Yousif et al., 2021): (a) ineffcient BD experts/data collectors, analysts, and presenters along with the dynamic nature (e.g. online data streams) of BD databases; (b) high expenditure in BD infrastructure/ experts, which will prevent enterprises from adopting BD technologies; (c) governments and corporations, which avoid sharing important data with the world, thus forcing data protection policies to be established.

Furthermore, another study specifcally looks at BD for energy effciency in building and notes data access challenges (Marinakis, 2020).

Finally, possible barriers are also related to social and environmental aspects in the context of BD. Aside from reducing energy consumption (Koukaras et al., 2021b), building renovations are typically motivated by issues such as structural repairs (D'Agostino et al., 2017). Buildings utilise 38% of EU energy and produce 36% of CO2. For example, the Dutch non-proft building stock's DR ratings for 2010–2014 were based on the energy performance of 850,000 homes. The data were obtained from a system that monitored 60% of the sector's buildings. Despite renovations, the dwellings' energy effciency did not alter much (Filippidou et al., 2017).

### 5.7 Future Developments

BD integration potentially benefts construction companies and all the other stakeholders involved at different stages of the DR life cycle. Using BD for business and environmental sustainability offers construction companies major prospects. BD may help the building sector overcome present hurdles. Using historical and current project data may assist in fostering long-term infrastructure. BD in construction helps prevent errors and yield better construction outcomes.

Future studies could investigate the integrated data that will be utilised for worldwide commercialisation of BD analytics for DR. This involves developing web/mobile applications that can be linked to BD integration systems to show real-time data analytics at a low cost, as well as work on the data-gathering process in the construction felds (Yousif et al., 2021).

Furthermore, future work foresees aspects related to (a) construction waste simulation tools, (b) BD analytics that enables linked building data platforms, (c) BD-driven BIM systems for construction progress monitoring, and (d) BD for design with data (Bilal et al., 2016a).

Future construction research will depend largely on BD since it can help develop better infrastructure and building designs. Construction must automate and integrate technologies to make BD utilisation simple and easy. BD technologies, BIM, and Computer-Aided Design (CAD) cannot be used without proper support and integration. The building industry's future rests on steadily improving the current conditions (Gbadamosi et al., 2020).

Finally, BD is essential to future building DR projects and data are essential for establishing training models and facilitating construction in general. Future improvements in this area will involve additional algorithms and models that depend on BD for reliable training.

#### 5.8 Conclusion

The objective of this chapter was to expose the reader to the concepts of BD and DR. Technologies, such as prefabricated exteriors, ICT-support for Building Management Systems (BMS), incorporation of Renewable Energy Systems (RES), BIM and building performance simulation models, and high-tech HVAC systems, are just not enough for reaching EU climate change policy goals by 2050. There are still open issues for future innovation in order to conceive effective policies and suggestions for DR implementations (D'Oca et al., 2018). Thus, BD analytics can be employed for the construction sector and more specifcally in the context of DR passing on the discussed advantages and benefts.

Nonetheless, the building sector has not yet fully embraced BD. The fast rise of this technology over the past two decades has increased the number of models and platforms for digitising diverse areas. The literature reveals several resources and platforms that may be used for construction management. Yet, currently there is poor adoption in DR and the building business must use and commercialise BD.

Future developments will beneft from internet tools and technologies that allow infrastructure modelling and CAD. These relate to the implementation of effcient energy measures, any prospects for climate change mitigation, and better management for thermal comfort in the context of BD and DR. Simple and inexpensive renovations frequently miss the opportunity to save more energy at a reduced cost. Any DR initiative should include many locations with different building, regulatory, market, and climatic conditions, thus involving BD.

The importance of BD and analytics for enhancing DR was highlighted using representative paradigms from the recent state-of-the-art. Social, economic, and environmental perspectives were also taken into account. In order to make the most out of the large amount of information accessible in the current BD environment, new analytical skills for DR must be developed.

#### References


Construction Waste Analytics (CWA): A conceptual framework. *Journal of Building Engineering, 6*, 144–156.


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Digital Twins and Their Roles in Building Deep Renovation Life Cycle

*Yuandong Pan, Zhiqi Hu, and Ioannis Brilakis*

**Abstract** Digital twins have started to diffuse within architecture, engineering, construction, and operations (AECO), based on their emerging and anticipated benefts to the various stakeholders involved in the building life cycle. However, their applications are still at an early stage, and much effort is still needed to exploit their full potential. This chapter explains some key notions to help understand digital twins in AECO. It exposes the various defnitions of digital twins and illustrates the basic steps and relevant methods for creating a digital twin. The chapter also provides an overview of the state-of-the-art deep learning methods for digital twins and discusses some real-life use cases. Finally, the chapter discusses the benefts and challenges associated with the adoption of digital twins.

Y. Pan

Technical University of Munich, Munich, Germany

Z. Hu (\*) • I. Brilakis

Construction Information Technology Laboratory (CIT Lab), University of Cambridge, Cambridge, UK e-mail: zh334@cam.ac.uk

<sup>©</sup> The Author(s) 2023

T. Lynn et al. (eds.), *Disrupting Buildings*, Palgrave Studies in Digital Business & Enabling Technologies, https://doi.org/10.1007/978-3-031-32309-6\_6

**Keywords** Scanning • Digital twinning • Geometry • Deep learning

### 6.1 Introduction

The construction sector remains one of the least digitised sectors. Digitalisation and automation can prove particularly valuable in overcoming a number of traditional challenges in architecture, engineering, construction, and operations (AECO). First, over half of the labour time is spent waiting for materials, equipment, and instructions on how to conduct the work during the construction stage, resulting in low productivity and shrinking proft margins. Second, many construction companies have suffered from underperforming projects, which leads to cost and schedule overruns and asset's quality issues. Third, many assets are designed for functional activities. Less consideration is given to their environmental impact leading to high carbon emissions and resource wastage. Fourth, due to skill shortage, it is diffcult to recruit enough construction professionals, such as supervisors, estimators, and engineers, which exacerbates the issue related to delays, asset qualities, and safety.

Digital twin (DT) is an emerging technological paradigm for achieving smart buildings, infrastructure, and cities (Tao et al., 2019). DT applications can facilitate project management in the AECO sector by increasing productivity and effciency. From manual drawings to computer-aided design, object-oriented design, and computational design, computer power is shaping the process of assets' construction and maintenance by encoding decision-makings through machine learning and other advanced technologies. This chapter aims to provide an overview of digital twins and their applications in the context of building renovation and discuss their main advantages, benefts, challenges, and barriers to adoption. The next section presents the defnition of digital twins. The following section presents the main steps for creating a digital twin. This is followed by the presentation of a series of use cases and some concluding remarks on potential future developments.

### 6.2 What Is a Digital Twin

According to Tao et al. (2019), a DT consists of three main elements: a physical product, a virtual representation of the physical product, and the connection that links these two parts together and enables data exchange and information sharing. The physical product refers to the actual asset built in the real (physical) world, which can also be defned as physical twin (PT). It can be a residential or a commercial building, a hospital, a school, a bridge, and so on. The virtual representation refers to the digital replica of the physical asset, which can exist throughout its life cycle. This data can be accumulated over time and updated at different stages of a physical asset's lifetime. The connection that links these two parts can be considered as an information exchanger to store, link, and update all product and process information over time. A DT can serve as an information repository for storing and sharing an asset's properties throughout its life cycle (El Saddik, 2018).

According to Sacks et al. (2020), a DT is dynamic and thus can be enriched through different stages of an asset's life cycle. Figure 6.1 depicts a typical life cycle of an asset PT and its DT from the design stage, through the construction stage, to the operation stage.

**Fig. 6.1** A typical life cycle of an asset PT and its DT from the design, construction, to the operation stage

At the design stage, the asset's designers start working on the conceptual plan. The asset's foetal DT contains both product and process information, where the former refers to different as-designed building information models (BIMs).1 Many of these models can be proposed at the beginning, but only the fnal client-approved design fle at the end of the design stage can be marked as "Design Intent", which means it will serve as a benchmark for evaluating the construction outcomes and can be considered as a guidance for the purpose of maintenance.

At the construction stage, the child PT contains off-site prefabricated assemblies and on-site constructed components. Therefore, the child DT consists of as-built product information and as-performed process information to mirror the asset's physical status at different steps during the construction stage. It should be noted that the product information and the process information accumulate over time into the child DT until the completion of construction. Each change will be updated in the asset's child DT to refect the as-is status and thus can facilitate progress monitoring and quality control.

Lastly, at the operation stage, the adult PT remains unchangeable status because of the completion of the construction. The asset's adult DT can support the analyses of performance, such as energy consumption and component maintenance. The collected data will be added to the as-maintained product to enrich the asset's adult DT. To conclude, an asset's DT should contain all information that represents the related physical information throughout its life cycle. Both the physical product and process will be assigned to the DT as a virtual copy throughout the asset's life cycle. Moreover, the logic of PT and DT can be extended to any type of physical entity, from small-scale manufactured objects to large-scale city-level objects. The product and process information contained in the DT should be determined by its purpose. Thus, a DT can be standardised and extensible to address current project management problems in the AECO sector.

# 6.3 Creating Digital Twins

As mentioned in the previous section, a DT contains product information and process information. A geometric DT (GDT) is fundamental as it is used to create links with process information during the asset's life cycle. Creating a GDT of an existing asset typically involves the following two

<sup>1</sup>Chapter 3 in this book provides a more detailed discussion on BIM.

steps: (1) capturing raw visual and spatial data in the form of RGB images and laser-scanned point clouds and (2) detecting geometric objects and relationships between objects. Step 1 of this process is signifcantly more automated than step 2, as shown by Agapaki and Brilakis (2021). Unfortunately, the effort and corresponding cost required to complete step 2 for most assets still represent a barrier to adoption as it may completely offset the value created by the geometric DT.

For data capturing (step 1), two major technologies are currently used to capture the geometry of an asset: laser scanning (terrestrial and mobile) and photogrammetry. The data generated should refect the physical surfaces of objects in the real world. Due to the discrete nature of the capturing techniques, the data provided by scanners is also discrete. Laser scanners generate point clouds that are sets of points in a 3D space. Each point is defned by three coordinates and additional information depending on the device used, which could be intensity, normality, and colour information, among others.

As for step 2, detecting geometric objects and their geometric relationships is still a time-consuming manual task. Lu et al. (2019), for example, scanned ten different road bridges and estimated that approximately 28 hours of work are required, on average, for the as-is modelling in contrast to 2.82 hours for data capturing. A number of leading 3D CAD companies (Autodesk, Bentley, ClearEdge3D, etc.) have developed software products that provide a variety of 3D modelling features which enable modelling from point cloud data. Agapaki et al. (2018) suggest that 64% of man-hour savings can be achieved by using state-of-the-art software supporting a semi-automated modelling process. However, 2382 manhours are still needed to model, for example, a small petrochemical plant with 240,687 objects and 53,834 pipes.

In order to reduce the human effort in creating a GDT, researchers have proposed a number of alternative approaches mostly focused on structural elements. Sanchez and Zakhor (2012) proposed a method that applies principal component analysis (PCA) and random sample consensus (RANSAC) to fnd relatively large-scale architectural structures, such as ceilings and foors. Monszpart et al. (2015) extracted planar structures in a point cloud that follows regularity constraints. They applied this approach in different scenarios, such as urban scenes, as well as the exterior and interior of buildings. Oesau et al. (2014) used horizontal slicing and then volumetric-cell labelling method. The volumetric cells are formulated as energy minimisation and solved by the graph-cut method. Xiao and Furukawa (2014) proposed a method called "inverse constructive solid geometry (CSG)" which detects planar surfaces and subsequently fts the cuboid primitives to the point cloud. Ochmann et al. (2016) proposed a method that explicitly represents buildings as interconnected volumetric wall elements. They determined the optimal room and wall layout by graph-cut-based multi-label energy minimisation. A method named voidgrowing method by Pan et al. (2021) aims to extract void room spaces in the point cloud frstly and subsequently extract 3D models of different objects.

Other approaches leverage prior knowledge to reconstruct walls and rooms. Stambler and Huber (2015), for example, proposed the concept of enclosure reasoning that defnes rooms as cycles of walls enclosing free interior space. Region growing is then applied to segment the point clouds, and simulated annealing is used to optimise rooms and walls. Tran et al. (2019) proposed a method called shape grammar to model indoor environments. They created 3D parametric models by placing cuboids into point clouds and classifying them into elements and spaces. The wall candidates are obtained from pairs of adjacent peaks in the histogram of point coordinates. Hu et al. (2022) provide a more in-depth review of this literature.

Deep learning (DL) is also widely applied to extract semantic information from spatial and visual data. VoxNet is proposed by Maturana and Scherer (2015) to detect classes of objects from point cloud data. It aims to predict a class label for the input. Volumetric grids representing the spatial occupancy are calculated frst and then applied to 3D CNNs. Qi et al. (2017a) instead proposed the frst neural network architecture, PointNet, designed for 3D deep learning in the point cloud. PointNet takes the point cloud as input and predicts labels for the entire input (point cloud classifcation) or labels for each point (point cloud segmentation). An improved version of the PointNet architecture called PointNet++ has then been presented by Qi et al. (2017b) and claims to provide better performance by considering spatial information of points in the point sets. These DL methods have been adopted in the AECO sector to facilitate GDT construction (Agapaki & Brilakis, 2020; Perez-Perez et al., 2021).

In summary, current approaches are still not fully automated, which means they still require human effort in the process of reconstruction. Their performance, especially when applied to a point cloud with high occlusions, would decrease due to the geometric occlusion of furniture. On the other hand, DL is an effcient and powerful tool that can be used to extract semantic information from the point cloud, but the lack of labelled data sets in the AECO domain causes diffculties with regard to training which in turn affects models' performance. In addition to this, the overall prediction performance differs signifcantly across categories, which makes it really hard to create a detailed GDT representing the current state of an asset when only considering the output of the DL methods.

#### 6.4 Digital Twin Use Cases

There are several use cases of DT in the construction sector, including construction progress monitoring, facilities management and operation, asset condition monitoring, sustainable development, and more. DT can provide reliable and useful information during a building's life cycle to AECO stakeholders.

DT can be applied to any physical asset at any given time. For historical assets which have been completed many years or decades ago and do not yet have any digital records, DT can help to start and keep a record of their performance for better maintenance and renovation. For facilities under construction, a dynamic DT can support real-time progress monitoring, quality control, diagnostics, and prognostics. In addition, DT can also be used in the future for capital investment projects before the design and construction of the facility, as it provides an effcient way to simulate the performance of a building and aid the decision-making process.

The way the physical and the digital twins are synchronised in real use cases depends on the purpose of the DT, which also determines the content of DTs (i.e., the elements and processes to be digitised, the level of detail required, how frequent the model is supposed to be updated, etc.). As the concept of digital twins is broad, it is impractical to propose a precise and detailed defnition of a digital twin that covers everything without considering its purpose. Some potential applications of DTs relevant to deep building renovations are presented hereafter.

#### *Example 1: Condition Monitoring*

A DT can be used to monitor the current condition of a building. By capturing geometric information through different sensors, the current condition of the asset can be visualised and represented by the DT. The geometry of facilities can be monitored by comparing the current condition with previous asset conditions over time, which allows a DT to give maintenance suggestions to the asset holders and managers (Hu et al., 2023).

Apart from monitoring the geometry change of a discrete asset, DT can also be used to monitor more complex large-scale systems, for example, the sewer system of a city. In this context, predictive maintenance operations can be utilised to identify potential blockages. Similarly, the current state of fow in pipes can be recorded and compared with the historical values to predict or locate disruptions in the system. Predictive maintenance recommendations or alerts can be sent to facility managers for more informed and timely decision-making.

#### *Example 2: Facility Management*

There is a very broad spectrum of facility operation, which includes but is not limited to operation management of mechanical, electrical, and plumbing (MEP) components in a facility (Z. Hu et al., 2016; Cheng et al., 2020), internal environment monitoring (Cao et al., 2015), and working productivity (Meerman et al., 2014). With the increasing adoption of the Internet of Things (IoT) and artifcial intelligence (AI) which are key components supporting DTs, facility management is becoming more and more intelligent. Similarly, augmented reality (AR) and virtual reality (VR) can be used in conjunction with DT to visualise the built environment and improve effciency (Baek et al., 2019; Chen et al., 2020; Chen et al., 2021; Zhang et al., 2020).

The concept of the digital twin is capable of embedding all these use cases in facility management according to the concept illustrated in Fig. 6.2. Relevant objects and values are captured and represented in a detailed digital model through capturing devices like laser scanners and cameras. By applying various IoT sensors such as thermometers, hygrometers, and carbon dioxide sensors, different values (like temperature, humidity, and carbon dioxide level) that represent internal environment conditions can be recorded and then updated in the digital model regularly. AI-relevant technologies can be used to help the process of creating the initial model as well as updating the model throughout a facility's life cycle. Facility managers can check the visually assistive information provided by AR and VR devices, which is able to lighten their workload and beneft working effciency. From small-scale facilities, like offces, to largescale urban environments, different sensors can be used to fnd how people exactly use these facilities and map occupant behaviour. With a better understanding of this data, the environmental conditions can be optimised, ultimately improving human wellness and living satisfaction.

**Fig. 6.2** Digital twin for facility management

#### *Example 3: Environment Simulation*

Digital twins can be used in the renovation phase of a project to simulate various scenarios without modifying the real asset. These scenarios can involve changing the natural light design, artifcial lighting, heating simulation, and so forth. By only modifying facilities in the DT, the impact of these changes can be understood without implementing the modifcations in the real world. VR/AR devices can make use of the DT to visualise the proposed designs and show the impact of changes and modifcations (e.g., lighting). This improves the decision-making of renovation and enhances the communication between designers and clients. For instance, different lighting atmospheres can be visualised, helping designers to aesthetically assess the design and present the outcomes of the setup to their clients (Natephra et al., 2017).

#### 6.5 Challenges to Digital Twin Adoption

Despite the fact that a DT is considered to offer benefts to all stakeholders of the built environment, some challenges hinder its adoption in real projects. Firstly, the effort involved in creating a digital twin is demanding, which undermines its feasibility and benefts. Many researchers are working on automating the process of digital twinning in the built environment in order to reduce human effort. The effort in the existing literature has been concentrated on reconstructing relatively large structural elements like ceilings, foors, and walls. MEP elements (such as fre alarms, emergency switches, etc.) should also be included in a DT, as these are essential elements for facility managers. In the repair and maintenance (R & M) activities of an asset, MEP costs usually constitute the largest share of the total cost (Adán et al., 2018). Therefore, a DT would be more valuable if it were to contain those elements. In addition, facility management also involves foor plans, space utilisation, asset location, and technical plants (D'Urso, 2011), which requires more accurate capture and modelling. Text information such as room numbers and serial numbers (IDs) of objects that can identify the corresponding asset instance is also benefcial, especially when managing large-scale facilities. These IDs represent the exact object instances in a facility and can be used to make the links between physical assets and DT much clearer. Currently, such activities are mainly performed manually in real projects. Some studies (e.g., Pan et al., 2022) in this area have started to emerge.

### 6.6 Conclusion and Future Direction

This chapter provides an overview of the background, defnitions, generation, and applications of DT in the built environment generally and building renovation specifcally. The state-of-the-art methods to create and update the geometry of digital twins were described. The potential applications of DTs, along with their advantages and current challenges, have been discussed with examples. The overarching conclusion is that DTs provide benefts and offer applications across the whole life cycle of built assets. Much research is still required to support the generation and the update of DTs, which is necessary to support the identifed applications and unlock their respective benefts.

In the built environment, how to generate and update DTs precisely and effciently to bring the benefts into real applications throughout the whole life cycle of a facility is still under research.

#### References


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Additive Manufacturing and the Construction Industry

*Mehdi Chougan, Mazen J. Al-Kheetan, and Seyed Hamidreza Ghaffar*

**Abstract** Additive manufacturing (AM), including 3D printing, has the potential to transform the construction industry. AM allows the construction industry to use complex and innovative geometries to build an object, building block, wall, or frame from a computer model. As such, it has potential opportunities for the construction industry and specifc applications in the deep renovation process. While AM can provide signifcant benefts in the deep renovation process, it is not without its own environmental footprint and barriers. In this chapter, AM is defned, and the main materials used within the construction industry are outlined. This chapter

M. Chougan • S. H. Ghaffar (\*)

Department of Civil and Environmental Engineering, Brunel University London, London, UK e-mail: mehdi.chougan2@brunel.ac.uk; seyed.ghaffar@brunel.ac.uk

M. J. Al-Kheetan

Department of Civil and Environmental Engineering, Mutah University, Mu'tah, Jordan

e-mail: mazen.al-kheetan@mutah.edu.jo

<sup>©</sup> The Author(s) 2023

T. Lynn et al. (eds.), *Disrupting Buildings*, Palgrave Studies in Digital Business & Enabling Technologies, https://doi.org/10.1007/978-3-031-32309-6\_7

also explores the benefts and challenges of implementing AM within the construction industry before concluding with a discussion of the future areas of development for AM in construction.

**Keywords** Additive manufacturing • 3D printing technology • Construction industry • 3D concrete printing

### 7.1 Introduction

Additive manufacturing (AM) is the process of fabricating threedimensional (3D) physical objects by connecting materials together in a layer-based manner following a specifc computer design (Guo & Leu, 2013). The concept of AM was frst introduced by Chuck Hull (1984), who used ultraviolet (UV) light to harden a layer of a liquid polymer (Wong & Hernandez, 2012). In recent years, AM has evolved to include a wide range of solutions and techniques, including selective laser sintering (SLS), direct metal laser sintering (DMLS), laser engineered net shaping (LENS), electron beam melting (EBM), fused deposition modelling (FDM), and digital light processing (DLP) (Albar et al., 2020). These methods enable the use of different materials in AM such as metals, composites, ceramics, and polymers and the production of end-parts that are capable of serving different purposes (Albar et al., 2020). The rapid development of AM has encouraged researchers and practitioners to adopt this technology in the construction sector as a cost-effective solution to create various structural components, regardless of their complexity, with minimum waste (Lyu et al., 2021).

In the construction sector, a signifcant focus of research and development was observed towards the development of different AM methods to cope with the unique characteristics of cementitious materials. These mostly include material extrusion and particle-bed processes as well as other generative approaches such as Smart Dynamic Casting (Paolini et al., 2019). Aggregate-based materials such as concrete are most commonly used in AM for the construction industry (Paolini et al., 2019). According to recent estimates, the value of the AM market for concrete printing was over \$310 million in 2019 and is expected to reach \$40 billion by 2027 with an annual growth rate of 116% (Pawar & Rohit Sawant, 2020). These fgures suggest that AM will be rapidly and globally adopted by the construction sector, driven by the promise of reduced environmental impact, support for more complex designs, and more cost-effective construction (Mart et al., 2022). It is important to note that while AM processes are less labour-intensive, the adoption of AM in construction is expected to result in signifcant job creation, including new high-value roles, for example, 3D printer manufacturing and maintenance engineers, mixture designers, materials suppliers, and specialist software developers (Avrutis et al., 2019).

The remainder of this chapter introduces and defnes AM and provides an overview of the main benefts of AM as well as its main applications in construction and deep renovation1 projects. Finally, practical challenges in the implementation of additive manufacturing are summarised, and upcoming advancements are briefy discussed in the fnal section.

## 7.2 Additive Manufacturing in Construction and Deep Renovation

Signifcant advancements have been made in concrete 3D printing in recent years thanks to the introduction of a variety of different materials in producing concrete mixtures. Ordinary Portland cement (OPC) was the frst material adopted by AM to produce full-scale printed concrete structures (Chougan et al., 2021). There are, however, concerns regarding the impact of OPC on the environment, which remains an issue with its implementation in AM. Cement production accounts for 5–7% of the total world CO2 emissions (Chougan et al., 2021). In order to achieve a sustainable built environment and reduce CO2 emissions, many researchers have suggested the implementation of alkali-activated materials (AAMs) as they can entirely replace OPC and produce a low-carbon binder (Chougan et al., 2021). In this case, materials such as metakaolin are used as aluminosilicate cementitious binders along with activators such as potassium silicate, sodium metasilicate, and potassium hydroxide to obtain AAM binders capable of building successful 3D printed structures (Alghamdi et al., 2019). Others have suggested enhancing AAMs' rheological properties by integrating modifying agents and additives in the mixtures like polypropylene (PP), polyvinyl alcohol (PVA), nano-graphite (NG), halloysite clay minerals, and attapulgite to improve the buildability, printability, and mechanical performance of AAMs for 3D printing (Chougan et al., 2021; Chougan et al., 2022).

The application of AM is not limited to the 3D printing of cementitious composites. Aside from cementitious composites, other categories of materials, such as polymers and metals, have also been used, particularly in renovation works. With the continuous development of AM technology,

<sup>1</sup>Chapter 1 in this book provides a detailed defnition of deep renovation.

the customisation of parts and components needed for particular purposes in renovation projects became possible. For instance, the production and installation of precast concrete façade sections can be particularly challenging due to their complexity and the wide variation in their confgurations in different buildings. In this context, AM could enhance the quality of the produced façade sections due to its higher degree of fexibility compared to standard production methods while also minimising postinstallation problems such as air and water leakages. AM can also be used to print moulds that have the ability to produce façade sections with effcient passive shading (Harris, 2022).

AM is being scaled increasingly. Big area additive manufacturing (BAAM), a 3D-printing process similar to FDM, has been developed to construct segments of cylindrical single-foored building components out of polymer materials such as neat ABS and CF-ABS (Biswas et al., 2017). In addition, robotic 3D metal printing, also known as wire arc additive manufacturing (WAAM), can be used to fabricate highly tailored and engineered steel connectors for large structures in the construction sector (Xin et al., 2021).

More examples of the cementitious composites, polymer, and metal additive manufacturing technologies in building structures can be found in Table 7.1.

# 7.3 Benefits of Additive Manufacturing in Construction

Historically, the construction industry was characterised by high energy consumption (i.e., 40% of the global energy consumption), high solid waste production (i.e., 40% of the global waste production), high greenhouse gases emission (i.e., 38% of the global CO2 emission), and high water depletion (i.e., 12% of the global water depletion) (Comstock et al., 2012). It has an undeniably high environmental footprint. Growing public interest in sustainability highlights the necessity for novel construction techniques and materials to mitigate traditional construction's high environmental impacts. AM technology represents one possible way for construction companies to use available resources more effciently. In fact, one of the main advantages of AM is the minimisation of raw materials consumption, which reduces the level of waste generated during construction (Yao et al., 2020; Valente et al., 2022).

A second related advantage of AM compared to traditional construction methods is the capacity to produce complicated large-scale structures


**Table 7.1** Various materials and technologies used in additive manufacturing

while also minimising raw materials waste by lowering or eliminating the necessity of conventional formworks (Wangler et al., 2016). The increasing use of cementitious materials (e.g., concrete) in construction, along with the high costs of formwork production, emphasises the value of additive manufacturing technologies in constructing complex structures. Furthermore, the ability to fabricate complex objects enables building structures to possess "multi-functionality" by facilitating the integration of services, including piping, insulation, and electrical setups, and offering a secondary function through its complex geometry, such as instinct thermal insulation (De Schutter et al., 2018). This may be particularly benefcial in the context of deep renovation where the number of building elements to be replaced is quite large and existing building constraints make the installation of different individual elements quite challenging. As the structure becomes more complex, AM technology becomes more advantageous. In the same way, AM may be less cost-effective and less environmentally benefcial for more "standard" designs (Labonnote & Rüther, 2017).

Finally, as AM processes remove the need for conventional energyconsuming processes and labour-intensive activities like concrete pumping and casting, shuttering, material logistics, and steel fxing, it reduces the costs of on-site assembly and construction, minimises human error, and improves productivity (Avrutis et al., 2019).

# 7.4 Practical Challenges for AM in the Construction Industry

Despite the benefts of AM to the construction industry, there are a series of major challenges to its implementation, which could hinder adoption. Firstly, the high cost of obtaining 3D printing equipment, as well as printers' transportation and logistics, could arguably represent a signifcant obstacle to the widespread application of 3D printing technology in the construction industry. Despite the technological advantages, many construction companies are still unable to justify or afford an investment in 3D printing equipment.

Secondly, while AM technology reduces human errors and the need for workers on construction sites, fnding qualifed individuals to work with AM remains diffcult (Deloitte, 2016). In addition, the shorter production and installation time comes at the cost of a longer design phase, which requires signifcantly higher effort and specialised modelling skills (Buswell et al., 2018). These labour supply problems are multifaceted. They are driven by a decline in the attractiveness of the manufacturing and construction sector, a lack of labour supply from the education sector with suffcient STEM skills and knowledge, a shortage of AM-specifc training programmes, and a general lack of AM knowledge and culture in many construction and construction-related manufacturing companies (Deloitte, 2016). Where skilled labour does exist, frms may face signifcant upskilling, skills maintenance, and retention challenges until the AM skills and training gaps are addressed (Deloitte, 2016).

Thirdly, there is a general lack of regulation, standardisation, and testing of AM printing structures and materials. Standards in AM facilitate technology adoption, boost confdence in the quality and safety of AM processes, materials, and outputs, and support the competitiveness of AM and construction companies (Martínez-García et al., 2021). While standards have been developed by a wide range of organisations, for example, the German Society of Mechanical Engineers, the ISO, and the American Society for Testing and Materials (ASTM), there would appear to be some challenges in aligning existing standards for testing the mechanical properties of more traditional materials and manufactured polymers and composites, and those generated through AM (Forster, 2015; Martínez-García et al., 2021). Indeed, it is fair to say that the fexibility AM introduces in terms of design and material use complicates testing and standards. Martínez-García et al. (2021) note that despite signifcant efforts by the ISO and ASTM, AM technology requires specifc standards in all the stages of the product development, including design, materials, manufacturing, and fnal part.

Finally, and somewhat contradictory to the benefts presented in the previous section, the environmental impact of AM may not be entirely positive. AM is still at an early stage of development and use in the construction sector. Much AM use still involves the use of environmentally hazardous substances (e.g., cement) in considerable quantities as well as substantial equipment and non-eco-friendly manufacturing (Agustí-Juan & Habert, 2017; Agustí-Juan et al., 2017). AM units are often powered by lithium batteries and the electricity consumption throughout the fabrication process may offset the waste reduction and the other environmental benefts generated by AM (Agustí-Juan & Habert, 2017; Agustí-Juan et al., 2017).

#### 7.5 Future Areas of Development

AM is particularly economically benefcial for large-scale building developments due to the enhanced geometrical freedom enabled by this technology. Compared to traditional construction methods, AM technology provides architecture designers the geometric freedom to create ideal complex structures while minimising the use of materials (Labonnote et al., 2016). However, while AM construction methods have been extensively adopted in real applications, there is still a lack of knowledge regarding large-scale AM. As a result, large-scale AM can be considered an escalated challenge compared to lab-scale 3D printing. Large-scale AM is typically more complicated than lab-scale 3D printing, as several practical construction challenges must be addressed. Large-scale AM involves a set of discrete technologies and thus requires consideration of a very different set of parameters, not least materials, reinforcing admixtures, economics, environmental optimisations, structural limitations, and 3D printing system design (Xiao et al., 2021). The majority of the existing studies concentrated on 3D printing of cementitious composites containing fne aggregate (i.e., mortar); however, cementitious composites with coarse aggregate (i.e., concrete) are attracting considerable interest because of their remarkable mechanical and cost-effciency advantages (Xiao et al., 2021). Therefore, further investigation is required to determine the impact of using coarse aggregates to move towards cementitious concretes in order to fulfl the large-scale 3D printing requirements.

4D printing, a novel approach that includes a fourth dimension (i.e., time and smart behaviour), can allow 3D-printed items to transform their geometry and behaviour throughout time in response to specifc conditions such as radiation, light, and temperature. The smart behaviour of 4D printing in shifting confgurations for self-assembly, multi-functionality, and self-repair is a crucial breakthrough in AM technology. While 4D printing delivers all of the advantages of 3D printing, its use in the construction sector is in its infancy, posing obstacles such as a considerable need for improved computer analysis, new design concepts, structure validation, and standardisation (Pan & Zhang, 2021).

### 7.6 Conclusion

AM technology represents a valuable innovation in the construction sector and is gaining popularity. There are many benefts to AM, such as its potential to signifcantly reduce the consumption rate of raw materials, reduce the generated waste during construction, lower CO2 emissions, reduce labour costs, minimise human errors, and improve productivity. Many complex designs, at a building or part-level, that previously were considered too problematic or costly for execution on-site can be easily implemented with the help of AM technology. Widespread adoption is not without challenges. In fact, some issues still exist in relation to process, materials, geometric complexity, software and building integration, and the standards associated with these elements. In order to capitalise on the impact of AM, additional research is needed to support the better integration of this technology in the construction sector. Moreover, to enable the rapid growth of this technology, standardised testing and quality control methods should be established to improve information sharing and benchmarking. Finally, without a pipeline of qualifed labour, the full potential of AM will not be realised. This will be a key challenge to overcome if the technology is to be pushed further into full-scale industrialisation.

#### References


Structural Engineering. https://repository.tudelft.nl/islandora/object/ uuid:b4286867-9c1c-40c1-a738-cf28dd7b6de5?collection=education


Strauss, H., & Knaack, U. (2015). Additive manufacturing for future facades: The potential of 3D printed parts for the building envelope. *Journal of Facade Design and Engineering, 3*(3–4), 225–235.

Technology, B. (2017). *Cellular fabrication*. http://www.branch.technology


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Intelligent Construction Equipment and Robotics

*Alessandro Pracucci, Laura Vandi, and SeyedReza RazaviAlavi*

**Abstract** With recent advancement in software, hardware, and computing technologies, applications of intelligent equipment and robots (IER) are growing in the construction industry. This chapter aims to review key advantages, use cases and barriers of adopting IER in construction and renovation projects. The chapter evaluates the maturity of available IER technologies in the market and discusses the key concerns and barriers for adopting IER such as the unstructured and dynamic nature of construction sites limiting mobility and communication of IER, hazards of humanrobot interactions, training and skills required for operating and collaborating with IER, and cybersecurity concerns. Finally, the chapter

A. Pracucci • L. Vandi (\*)

Focchi Spa Unipersonale, Poggio Torriana, Italy e-mail: a.pracucci@focchi.it; l.vandi@focchi.it

S. RazaviAlavi

© The Author(s) 2023

111

Department of Mechanical and Construction Engineering, Northumbria University, Newcastle upon Tyne, UK e-mail: reza.alavi@northumbria.ac.uk

T. Lynn et al. (eds.), *Disrupting Buildings*, Palgrave Studies in Digital Business & Enabling Technologies, https://doi.org/10.1007/978-3-031-32309-6\_8

proposes a framework for implementing IER that helps in their benefts by defning relevant metrics while considering their pitfalls in terms of quality, safety, time, and cost. This framework assists practitioners in decisionmaking for adopting IER in their construction operation.

**Keywords** Robotics • Construction • Safety • Monitoring • Quality control • Assessment framework

# 8.1 Key Definitions and Concepts

Table 8.1 provides a summary of key defnitions and concepts related to the use of intelligent construction equipment and robotics in the construction industry.



(*continued*)

**Table 8.1** (continued)


#### 8.2 Introduction

The construction industry plays a crucial role in ensuring job creation, driving economic growth, and providing solutions to address environmental, social, and economic challenges. The market value of the construction sector represents between 9% and 15% of GDP in most countries (Davila Delgado et al., 2019). Despite its huge economic importance, the construction industry is traditionally slow to change and consequently beset with ineffciencies resulting in lower productivity levels compared to other sectors (Davila Delgado et al., 2019). However, despite the complexity and fragmentation of the construction industry and the diffculties of coordinating the wide numbers of players and their tasks that slow down the introduction of innovative solutions, the construction sector has evolved in the last 25 years. This is especially driven by digital technologies and automation providing the construction industry with an opportunity to fnd innovative solutions to some of its rooted challenges. These innovations spanned across the whole project lifecycle, from design and engineering, through manufacturing and construction, to operation and maintenance, and retroft/reuse/end-of-life. Among these, robotics is an emerging technological branch that can have an impact in construction areas such as off-site production, installation activities on-site, and operation and maintenance. This chapter will provide key insights about the digital transformation enabled by IER solutions in construction sites, analyze their current applications, limitations, and future developments, and propose an assessment framework to support construction actors in the decision-making process into the gradual adoption of IER for performing specifc tasks.

### 8.3 Advantages and Benefits of IER

#### *8.3.1 Improving Safety*

The incident rate in the construction industry is the highest among various major industries in many countries (Choi et al., 2011). In the US, 25% of the fatal work injuries in 2020 belong to the construction sector (U.S. Bureau of Labor, 2021). In Great Britain, 1.8% of the construction workers reported a musculoskeletal disorder, which is the highest rate among the industries with similar work activities (Health and Safety Executive, 2021). Replacing humans by semi-autonomous and autonomous robots for undertaking unsafe tasks can reduce the number of incidents (Ilyas et al., 2021). Robots can be used for automating unsafe activities including heavy lifting and on-site inspection in dangerous work environments such as underground mines (Zimroz et al., 2019) and bridges (Lin et al., 2021). To reduce musculoskeletal injuries and physical fatigue of construction workers caused by repetitive and prolonged manual tasks, exoskeleton is being used for augmenting workers' physical ability (Brissi et al., 2022). Safety inspections and monitoring are other tasks that can be automated by robots for detecting unsafe locations (Martinez et al., 2020) and Personal Protective Equipment (PPE) on job sites (Ilyas et al., 2021).

#### *8.3.2 Improving Productivity*

Productivity growth has been a major concern in the construction industry as it was only one-third of the average total economy productivity growth over the past 20 years (Ribeirinho et al., 2020). Productivity of the construction industry can be improved by automating and robotising repetitive and labour-intensive activities. Autonomous transportation of construction materials by robots can improve productivity and eliminate human errors in these processes (Chea et al., 2020). For heavy lifting, robotic crane systems could improve productivity by 9.9–50% (Lee et al., 2009). The examples of IER applications for automation of different construction activity types are presented in Table 8.2.

#### *8.3.3 Addressing Skilled Worker Shortage*

Skilled worker shortage has been one of main issues in the construction industry over the past few years (Kim et al., 2020). The growing demand of construction workers and the aging workforces in many countries such as the UK (CITB, 2021; Green, 2021) are the main contributors to the


**Table 8.2** IER application for improving productivity of different types of construction activities

skilled worker shortage. In the long term, leveraging construction automation and replacing humans with IER can address this issue (Melenbrink et al., 2020). In addition, use of IER can address the challenges of the high labour wage in construction projects particularly in the metropolitan areas (Pan et al., 2020).

# 8.4 Key Use Cases for Intelligent Construction Equipment and Robotics

Although the impact of IER has not yet been fully realised in the construction industry (Carra et al., 2018), their applications are emerging to enhance construction productivity, safety management, quality control, and site planning issues. The frst examples of construction robots were seen in the Japanese construction industry in the late 1970s and 1980s to supplement and replace workforce (Yilmaz & Metin, 2020). Construction automation and robotics application are classifed in this chapter according to:


#### **Table 8.3** Description of construction phase for IER classifcation

Off-site application *Off-site* construction is widely used since the adoption of prefabrication approaches increase the control and the quality of the technological component manufactured. Indeed, the activities are conducted in a controlled environment as a factory with the consequence of reducing the risk of low quality during on-site installation. The adoption of IER solutions in a factory moves construction toward an industrialised sector with well-consolidated off-site activities. On-site application *On-site* execution is still a manual activity in many cases with the consequences of leading to problems such as unpredictable tasks and low levels of accuracy (Davila Delgado et al., 2019). The tasks during on-site stage are focused on the correct product installation, keep control of tasks advancement and monitoring with inspections activities the quality results. The traditional on-site activities require an appropriate level of labour skills to achieve the

quality (Yilmaz & Metin, 2020). On-site applications include:

• **Construction**—phase which involves the installation of different materials and construction actions (bricks laying, concrete formwork, timber frame as described in Table 8.2).

necessary effciency in terms of construction duration and cost, and building


#### **Table 8.4** Description of autonomy level for IER classifcation


**Fig. 8.1** Allianz Tower while human workers are cleaning the façade. (Credit: Piermario Ruggeri-Focchi façade)

IER technologies can be further classifed based on their technology readiness level (TRL) which identifes the maturity of the technologies within the market. In particular:


Table 8.5 shows TRL for different IER technologies. The TRL level has been assigned based on market and academic research.




The next subsections present some key examples of IER applications in the construction industry to highlight their signifcant impacts on various aspects of construction projects.

**Additive manufacturing for construction phase**—*MX3D Bridge* is a pedestrian bridge designed with generative design—complying between sustainable aspects and structural needs—and manufactured by exploiting the synergies between robotic and additive manufacturing. This is one of the frst impactful examples for metal components moving from intelligent design to robotic-based production, validating the notion of the ability of such systems to move the construction sector into industrialised construction (MX3D Bridge, 2020) (Figs. 8.2 and 8.3).

**Automatics monitoring for inspection**—The potential of the combination between digital platform and inspection robotics is providing new opportunities for construction. This is well represented by the collaboration of Boston Dynamics and its sophisticated and movable robots SPOTWALK with HOLO BUILDER platform for the site project management controls which is revealing new digital workfows in the construction sector (HoloBuilder and Boston Dynamics Launch SpotWalk for Autonomous Reality Capture | Geo Week News | Lidar, 3D, and More Tools at the Intersection of Geospatial Technology and the Built World, 2020) (Figs. 8.4 and 8.5).

**Unmanned Aerial Vehicle (UAV) for maintenance activities**— UAVs could reach hazardous or high places, which is becoming a diffused

**Fig. 8.2** MX3D Bridge. (Photo by Joris Laarman Lab)

**Fig. 8.3** MX3D Bridge. (Photo by Adriaan de Groot)

**Fig. 8.4** Spot robot for autonomous 360° photo capture. (Image courtesy of HoloBuilder)

practice with heightened expectations considering the opportunities that these technologies open to control the health of built assets. For instance, *Elios* is a UAV tool which inspects the photovoltaic (PV) panels with the aim of tracking and monitoring each cell to discover irregularities or loss of performances (Elios Aerial Thermography, 2021) (Figs. 8.6 and 8.7).

**Fig. 8.5** HoloBuilder SpotWalk integration with Boston Dynamics. (Image courtesy of HoloBuilder)

**Fig. 8.6** Thermography inspection of a PV plant by drone

**Robotics arm in construction phase**—*MULE* is a construction robot, fexible, portable, job-site ready lift assist which reduces time for lifting activities by 80% (MULE Lifting System | R.I. Lampus, 2021). ROB-Keller System AG have designed Robotic brickwork, *Rob*, to control the positioning of the masonry entirely positioned and controlled by the robotic arm. *Rob* allows to build walls even with shapes in compliance with

the calculations and resistance simulations made in the design phase (Robotic Brickwork, 2021).

**Vehicles for construction phase**—*HX2* is an autonomous and electric load carrier that can move heavy construction components. It has a vision system that allows the robot to detect humans and obstacles (Volvo CE Unveils the next Generation of Its Electric Load Carrier Concept, 2020).

**Exoskeleton**—*Eksovest* is an upper-body exoskeleton that supports arms during lifting activities (Exoskeletons Trialled on UK Construction Sites, 2021). *Exopush*, *developed by* Colas, is an exoskeleton designed to give power assistance to operatives working leveling with a rake. The exoskeleton improves the worker posture by reducing the stress movement of 30% (Colas Introduces the Exopush Exoskeleton to the UK, 2021). *G*-Ekso bionics has developed a robot which is able to hold heavy tools on aerial work platforms like scissor lifts and to standard scaffolding (EksoZeroG—Zero Gravity Tool Assistance, 2021).

**Integrated solution**—*Hephaestus*—A H2020 co-funded project has designed an IER tool for the installation of prefabricated building envelopes (Elia et al., 2018; Highly AutomatEd PHysical Achievements and PerformancES Using Cable RoboTs Unique Systems | HEPHAESTUS Project | Fact Sheet | H2020 | CORDIS | European Commission, 2020). The Hephaestus robot is composed of a cable-driven parallel robot (CDPR) and a modular End-Effector kit (MEE) which host tools and devices for the bracket positioning and façade modules installation. This robot expects in the next few years to provide a market autonomous solution for on-site tasks for installation of prefabricated envelopes, focusing on highly risky and critical construction tasks. The long-term purpose is to adopt Hephaestus not only for the installation stage but also for operations of maintenance and façade module replacement (Figs. 8.8 and 8.9).

**Fig. 8.8** Cable-driven parallel robot installed in the demo building. (Credit Alex Iturralde)

**Fig. 8.9** Hephaestus details during façade installation. (Credit Alex Iturralde)

# 8.5 IER for the Renovation Phase

In Europe more than 70% of the building stock was built before the 1970s and suffers from poor energy performance. Renovation is a key strategy to reduce the energy impact and the carbon footprint of buildings. The European Commission's target is to retroft at least 3% of the building stock market by 2030. The retroftting intervention involves changing in the building confguration to improve the energy performance while maintaining the occupant's comfort (Green Building 101, 2014). In this scenario construction automation and robotics can accelerate retroftting interventions. For example, robotics applications support the existing workforce with on-site activities, which are currently based on craftsoriented processes (Tellado, 2019). However, current key advantages of using robotics in retroftting projects are focused on building data collection especially for the planning and design phase such as:


Robotics applications play a crucial role in addressing the challenges of building energy retroft (Mantha et al., 2018). Accurate measurements, real time, and instant transfer of data can be integrated in the Building Information Modeling (BIM)1 and exploited by relevant IER operations. A generic framework could be developed to support the data collected to arrive at an optimal building retroft decision (e.g., most economical and most energy saving). Some examples are *Bertim* (*Refurbishment Solutions | STUNNING*), which is a H2020 project that aimed to enhance a building retroftting intervention by integrating automation applications in the process, and *Vertliner* (*VERTLINER*)—an application-focused autonomous UAV that navigates inside the building, acquiring precise 3D data, images, or videos—to inform and update several layers of digital twin models and BIM representing the indoor environment.

<sup>1</sup>Chapter 3 in this book present BIM in more detail.

#### 8.6 Challenges and Barriers

Despite the advantages and benefts of IER, the construction industry has faced several challenges and barriers with their adoption as summarised in Table 8.6.


**Table 8.6** Challenges for adopting IER in the construction industry

a Chapter 9 in this book provides a more extensive discussion on cybersecurity and privacy considerations for deep renovation

# 8.7 Frameworks for Assessing and Implementing IER

A systematic approach to guide IER implementation is still missing in the construction sector (Hu et al., 2021; Pan et al., 2018). This section proposes a preliminary framework of indicators for assessing the advantages of using IER for buildings based on the current construction needs. The framework is designed for construction companies interested in evaluating whether robotic applications facilitate their planned tasks according to specifc tasks' indicators. Using the selected metrics, the framework compares between the current manually handled tasks with the ones achievable by the adoption of a selected robotic technology. Hence, a quantitative ranking is used for the different tasks assigning a score for key macro indicators (quality, safety, time and cost) with the following scores:


The total of all scores is a preliminary result to evaluate the IER for the selected activity: if the total score is positive, IER could facilitate the construction work, and if the total score is negative, IER will not improve the construction work.

The assessment framework is a preliminary decision support tool to facilitate the evaluation about advantages for IER adoption. More detailed investigation will need to be implemented to boost IER technologies adoption, especially once more solutions are available on the market. At this stage, the proposed framework can be considered an early-stage tool for navigating the advantages of emerging IER applications in the construction industry (Table 8.7).


**Table 8.7** Framework of indicators for assessing the advantages of using IER for buildings

#### 8.8 Conclusion

There is emerging evidence that IER can beneft on-site and off-site construction operations. However, there are some challenges and barriers to overcome. From a contractor-side, economic factors including the high capital costs along with the costs pertaining to training and upskilling workers to operate IER are the main challenges. The nature of construction sites, which is generally unstructured, complex, and dynamic, entails further safety and operational challenges for using IER. Moreover, inadequate digitalisation levels within the construction industry limit the utilisation of IER. Tools for comparing traditional methods with advanced IER technologies are lacking in the construction industry. To contribute to these important gaps, this chapter classifed the application of IER, reviewed key emerging applications and technologies, and proposed a framework to help assess the feasibility of implementing IER in construction. While some challenges to the adoption of IER are likely to persist in the short and mid-term, the emerging opportunities opened by IER have started to offer evidence about their disruptive nature and positive impact to quality, safety, and productivity in this key industry.

#### References


nologies to enhance safety, health, and performance in construction: Industry perspectives and future research directions. *IISE Transactions on Occupational Ergonomics and Human Factors, 7*(3–4), 185–191. https://doi.org/10.108 0/24725838.2018.1561557


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Cybersecurity Considerations for Deep Renovation

*Muammer Semih Sonkor and Borja García de Soto*

**Abstract** Deep renovation efforts to improve the energy performance of buildings are of paramount importance for the overall energy reduction of nations. Like other construction projects, deep renovation ones are affected by the digital transformation of the construction industry. While this transformation involves the increasing utilisation of new technologies to optimise cost, time and quality at every stage, concerns emerge about how to maintain robust cybersecurity. This chapter summarises the cybersecurity research related to each deep renovation phase and provides an overview of relevant cybersecurity frameworks, standards, guidelines and codes of practice. The chapter also discusses the need for a contingency approach in deep renovation cybersecurity due to the varying requirements of each project and organisation.

M. S. Sonkor • B. García de Soto (\*)

S.M.A.R.T. Construction Research Group, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, United Arab Emirates e-mail: semih.sonkor@nyu.edu; garcia.de.soto@nyu.edu

<sup>©</sup> The Author(s) 2023

T. Lynn et al. (eds.), *Disrupting Buildings*, Palgrave Studies in Digital Business & Enabling Technologies, https://doi.org/10.1007/978-3-031-32309-6\_9

**Keywords** BIM • Construction 4.0 • Cybersecurity • Cyber-physical systems • Digitalisation • Information technology (IT) • Internet of Things (IoT) • Operational technology (OT)

### 9.1 Introduction

Sustainable development constitutes one of the highest priority topics on most national agendas, and energy effciency has a critical role in achieving sustainability targets. Buildings consume a signifcant amount of energy (Lynn et al., 2021); therefore, reducing the energy consumption of existing buildings can help countries achieve these targets and enhance global energy effciency. Shnapp et al. (2013, p. 19) defne deep renovation as "a renovation that captures the full economic energy effciency potential of improvement works, with a main focus on the building shell, of existing buildings that leads to a very high-energy performance"1. While widely referenced, it is important to note that there is no consensus on the defnition of deep renovation and the associated minimum energy reduction required.

Deep renovation can be considered a specialised subcategory of construction. It thus passes through similar stages (e.g., design, construction/retroftting, operation and maintenance (O&M) and end of life) as with other construction projects, even though its scope involves retroftting existing buildings rather than building one from the ground up. Therefore, technological advances in the construction industry and the concerns related to these advances are also applicable to deep renovation projects. The digitalisation that the construction industry is going through, often referred to as Construction 4.0 (Klinc & Turk, 2019), affects the information generated and used and the physical tasks performed during the construction and O&M phases (Garcíade Soto et al., 2020). While this transformation improves the cost and time effciency of processes and construction quality, it also leads to substantial cybersecurity concerns, as with other digitalised industries. The convergence of information technology (IT) and operational technology (OT) (Harp & Gregory-Brown, 2015) further exacerbates the diffculty and complexity of addressing such concerns. Furthermore, safety issues

<sup>1</sup>Chapter 1 in this book provides a more detailed discussion on the defnition of deep renovation.

arise due to the increasing use of OT to perform (e.g., autonomous excavators to handle earthworks) and monitor (e.g., autonomous site monitoring devices) site activities (Sonkor & García de Soto, 2021). As a result, the signifcance of providing robust cybersecurity increases during all phases of construction projects to prevent the exposure of sensitive information and any potential physical damage.

The rest of this chapter is organised as follows. Next, we discuss major types of cybercrimes that affect the construction industry and the related laws and regulations. We then outline some prominent cybersecurity standards, codes of practice and frameworks applicable to the construction industry. Following a review of relevant cybersecurity research organised by the deep renovation phase, we explain the need for a contingency approach to cybersecurity in the construction industry that takes into account the differences in projects, organisations and contexts while highlighting that there cannot be a one-size-fts-all solution for all different sizes of companies and deep renovation projects.

## 9.2 Cybercrimes and Cybersecurity in Construction

Increased connectivity, remote working and the increasing sophistication of malicious actors are contributing to a rise in cybercrime (FireEye, 2021). The construction sector is not insulated from this trend. As more and more buildings become reliant on remotely operated software systems and the Internet of Things, the attack surface and associated vulnerabilities and risks increase. Construction companies and their employees, specifc projects and building systems have been targeted by a wide range of cyberattacks, including phishing, ransomware, denial of service, identity theft and other types of unauthorised access (Nordlocker, 2021; Korman, 2020; Turton & Mehrotra, 2021; Rashid et al., 2019). While fnancial gain is a common motivation for such attacks, it is not always the case. For example, in 2016, hackers launched a distributed denial of service (DDoS) attack on two residential buildings in Finland by temporarily disabling the computer systems that controlled the heating and hot water distribution systems, resulting in obvious inconvenience and distress for residents (Ashok, 2016). Unsurprisingly, governments worldwide have responded to the threat of cyberattacks. These actions include enacting new laws focusing on cybercrimes and introducing acts and regulations that defne criminal offences and the related sanctions. Notwithstanding this, few are


**Table 9.1** Common types of cybercrimes, examples from the construction industry and related laws and regulations

1990


**Table 9.1** (continued)

specifcally focused on the construction industry and buildings per se. Table 9.1 summarises common cybercrimes, examples from the construction industry and related laws and regulations.

# 9.3 International Standards, Best Practices and Cybersecurity Frameworks

In recent years, national and international institutions have been active in producing standards and guidelines to support companies in assessing their current cybersecurity levels and setting targets for the future. While the overwhelming majority are aimed at the IT sector or frms in general, there are several codes of practice and guidelines aimed at the architecture, engineering, construction and operations (AECO) sector specifcally. As modern buildings make widespread use of automation and control systems, for example, for heating, and such systems have been the target


**Table 9.2** Summary of the commonly used cybersecurity standards and procedures

(*continued*)


**Table 9.2** (continued)

a Indicates the last publication date of an ISA/IEC 62443 Series (in this case, Part 3-2: Security risk assessment for system design)

of cyberattacks, standards and guidelines for the security of such systems are also relevant. While some are industry-specifc, others were designed in a generic way to cover a wide range of sectors. Some of the commonly used standards and procedures for cybersecurity are presented in Table 9.2.

In addition to standards and protocols for security and control systems, there are several codes of practice and guidelines. Some are general (for any industry), but others specifcally address the construction sector. While codes of practice do not purport to replace standards, they provide guidance and support for achieving standards. Table 9.3 summarises some of the prominent codes of practice, guidelines and frameworks for cybersecurity.

## 9.4 Related Cybersecurity Research by Renovation Phase

To date, scholarly research has focused primarily on the advantages and potential benefts of increased digitalisation of the construction sector. In comparison, cybersecurity aspects have received less attention. There are notable exceptions. For example, Turk et al. (2022) proposed a systematic framework to address the cybersecurity problems specifc to construction projects. Their framework identifed cybersecurity as "the absence of the three wrongs across the four kinds of elements" (Turk et al., 2022, p. 1). The three wrongs refer to stealing, harming and lying. The four elements


**Table 9.3** Summary of the commonly cite codes of practice, guidelines and frameworks for cybersecurity

(*continued*)


**Table 9.3** (continued)

(*continued*)


**Table 9.3** (continued)

that might be affected by such wrongful activities are material, information, person and system. After defning cybersecurity, they customised the framework to refect construction-specifc characteristics. These characteristics include the multi-stakeholder settings of projects, overlapping boundaries of different entities involved in different projects and having distinct stages (e.g., design, construction and O&M) with particular challenges.

Several studies in recent years have discussed various aspects of construction cybersecurity and suggested solutions across the construction and deep renovation life cycle. Zheng et al. (2019) stressed the lack of studies concerning the information security aspects of BIM during the design and planning phase. In order to improve confdentiality and reduce the risk of data breaches, a context-aware access control model named CaACBIM was proposed. Mantha et al. (2021) pointed out that the sensor data collected during the commissioning phase can be altered by malicious actors (e.g., an owner with a malicious intention or a competitor). In order to address this threat, they proposed utilising an autonomous robotic system for randomised check-pointing and illustrated its feasibility with an example.

Modern construction and retroftting make increasing use of (semi) autonomous and remote-controlled equipment (Sonkor & García de Soto, 2021). This includes the use of complex cyber-physical systems, such as industrial machinery and vehicles (e.g., cranes), exoskeletons, unmanned aerial vehicles (UAV),2 on-site and off-site automated fabrication and additive manufacturing,3 to name a few. Notwithstanding the pervasiveness of such equipment, a recent survey of cybersecurity research on such construction equipment by Sonkor and García de Soto (2021) revealed a paucity of studies.

Many of the construction cyberattacks identifed in Table 9.1 occur in the O&M phase of construction projects, particularly in smart buildings. Pärn and Edwards (2019) presented the potential cybersecurity issues for CIs during the O&M phase and suggested using blockchain technology for data exchange and storage as a mitigation action. Several studies focused on the cybersecurity aspects of smart buildings. Wendzel et al. (2014) discussed botnets' abilities to control and monitor building automation systems (BASs) and their potential damage to the built environment. On a related topic, Mundt and Wickboldt (2016) undertook a study to identify the cyber risks, possible attackers and attack vectors related to BASs. They presented the security gaps found in two case studies to prove that additional attention is required to ensure robust BAS security. Mirsky et al. (2017) showed how air-gapped building management networks could be attacked using a compromised heating, ventilation and air conditioning (HVAC) system. Lastly, Wendzel et al. (2017) investigated the potential attacks against smart buildings and proposed solutions to protect them.

Interestingly, few studies explore the end-of-life phase of buildings and construction projects from a cybersecurity point of view. As building systems may retain sensitive data that can be exposed due to vulnerabilities, care needs to be taken to ensure suitable cybersecurity safeguards are in place.

<sup>2</sup> See Chap. 8 in this book for a more detailed discussion.

<sup>3</sup> See Chap. 7 in this book for a more detailed discussion.

While it is useful from a research perspective to use a phased approach to identify gaps in the literature, many actors and systems in the construction and renovation process are present across the entire life cycle, particularly as a consequence of digitisation. As such, full life-cycle approaches to cybersecurity assessment and associated research are needed. For example, Mantha and García de Soto (2019) investigated the vulnerability of different project participants and construction entities during the different phases of the life cycle of construction projects as a consequence of Construction 4.0. Their study considered potential risks and provided a basis for assessing the impact of interactions in a digital environment among different project participants. Considering the increasing use of IoT, edge computing and artifcial intelligence (AI) and the likelihood that every stage of construction and deep renovation projects is expected to rely on these technologies in the near future, their cybersecurity vulnerabilities and risks require more attention (Ansari et al., 2020).

#### 9.5 The Need for <sup>a</sup> Contingency Approach

The primary purpose of all the previously mentioned cybersecurity standards, frameworks, guidelines and academic studies is to improve the cybersecurity level of projects and organisations. However, considering the variety in functions, roles and scale differences in construction and deep renovation frms and projects, a one-size-fts-all cybersecurity approach may not be desirable or feasible. For example, public companies will have to meet specifc accounting standards to ensure adequate controls are in place, and multinational frms may have to deal with a wide range of cybersecurity and data protection requirements. Similarly, specialist craft renovations are likely to have different cybersecurity requirements and demands than more generic and large-scale construction/renovation projects. Each stakeholder constitutes a different cyber risk, and each one has various cybersecurity concerns. Therefore, care needs to be taken to ensure that an appropriate cybersecurity assessment and associated controls are put in place that can accommodate the range of projects and frms that characterise the sector.

#### 9.6 Conclusion

The integration of construction and digital technologies such as IoT, machine learning and cloud computing disrupts how construction projects are planned, constructed and operated, making the construction industry and buildings easy targets. At the same time, the sophistication and volume of cyberattacks are increasing. As an inevitable consequence, maintaining robust cybersecurity becomes an everyday challenge. Deep renovation projects face the same hurdles as any other construction project when it comes to protecting sensitive information and maintaining safety. This chapter provides an overview of the cybersecurity efforts in the construction industry and deep renovation and presents relevant frameworks, standards, codes of practice and research. Furthermore, it discusses the need for a contingency approach while considering the cybersecurity requirements of deep renovation projects and the frms that deliver them. There is no silver bullet in cybersecurity. Cybersecurity considerations and related actions should be an indispensable part of deep renovation projects from planning to the end of life, taking into account the needs of all stakeholders.

**Acknowledgements** The authors would like to thank the Center for Cyber Security at New York University Abu Dhabi (CCS-NYUAD) for the support provided for this study.

#### References


deterrence. *Engineering Construction and Architectural Management, 26*(2), 245–266. https://doi.org/10.1108/ECAM-03-2018-0101


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Financing Building Renovation: Financial Technology as an Alternative Channel to Mobilise Private Financing

*Mark Cummins, Theo Lynn, and Pierangelo Rosati*

**Abstract** Access to capital is one of the key barriers for deep renovation. This chapter presents the potential advantages and benefts that fnancial technology (FinTech) solutions such as crowdfunding and blockchainbased solutions such as tokenisation and smart contracts can provide to building owners and construction companies in terms of fnancing. Future avenues for research in this space are also presented.

M. Cummins

T. Lynn Irish Institute of Digital Business, DCU Business School, Dublin City University, Dublin, Ireland e-mail: theo.lynn@dcu.ie

P. Rosati (\*) J.E. Cairnes School of Business and Economics, University of Galway, Galway, Ireland e-mail: pierangelo.rosati@universityofgalway.ie

© The Author(s) 2023 T. Lynn et al. (eds.), *Disrupting Buildings*, Palgrave Studies in Digital Business & Enabling Technologies, https://doi.org/10.1007/978-3-031-32309-6\_10

Strathclyde Business School, University of Strathclyde, Glasgow, UK e-mail: mark.cummins@strath.ac.uk

**Keywords** FinTech • Crowdfunding • Blockchain • Smart contracts • Alternative fnance

#### 10.1 Introduction

Moves towards a long-term net zero emissions objective are complex and multifaceted. One part of this global picture that needs to be addressed effectively is the high level of energy ineffciency amongst a high proportion of buildings globally. For the EU, it was estimated, for instance, that (as of 2011) approximately 75% of the building stock in the EU required some form of energy effciency upgrade in the form of retroftting and renovation (Economidou et al., 2011). The Energy Effciency Directive (Directive 2012/27/EU) of 2012 has been a key policy response by the EU to set the foundations for a signifcant programme of building renovation.1 This legislation was partially revised in 2018. However, the European Commission has now commenced a process of overhauling the entire Energy Effciency Directive,2 seeking to leverage the Renovation Wave strategy announced in 2020.3 This latter strategy aims to double annual energy renovation rates in the next 10 years. As well as reducing emissions, these renovations will enhance quality of life for people living in and using the buildings, and should create many additional green jobs in the construction sector.

Feedback in the Open Consultation on the Renovation Wave suggested that lack of or limited resources to fnance building renovation is one of the most important barriers to building renovations. These barriers include a lack of fnancial incentives, access to mainstream fnancing products, and funding for publicly owned buildings. In response, ensuring adequate and well-targeted funding is central to the EU Renovation Wave strategy. Despite this, while the European Commission highlights the need for greater adoption of digital and innovative technologies in the construction sector and identifes specifc digital tools and technologies, it is silent on fnancial technologies and how they might reduce barriers to building and renovation fnance.

<sup>1</sup> https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2012:315:000 1:0056:en:PDF.

<sup>2</sup> https://ec.europa.eu/info/news/commission-proposes-new-energy-efficiencydirective-2021-jul-14\_en.

<sup>3</sup> https://energy.ec.europa.eu/topics/energy-efficiency/energy-efficient-buildings/ renovation-wave\_en.

Against this backdrop, this chapter will explore the fnancing of building renovation and how innovations in the fnancial technology (FinTech) space may serve to mobilise more private fnancing. The remainder of this chapter is organised as follows. Following a summary of recent literature on fnancing building renovation, we defne and outline key FinTech concepts and technologies. We then explore two of the most prominent FinTech solutions—crowdfunding and blockchain-based solutions.

## 10.2 Deep Renovation Financing: Key Terms and Concepts

Economidou et al. (2019) provide an overview of the main fnancing instruments available to support energy renovations in the EU (Fig. 10.1). These are categorised by type of fnancing instrument, spanning (1) nonrepayable rewards, (2) debt fnancing, and (3) equity fnancing, and by market saturation, spanning (1) traditional and well-established, (2) tested and growing, and (3) new and innovative fnancing mechanisms. A brief defnition for each instrument is provided in Table 10.1 with other key terms and concepts used in this chapter.

Kunkel (2015) sets out the barriers to traditional investment in building renovation as follows: (1) upfront investment and the bankability of projects; (2) information asymmetry; (3) the quality of the on-site implementation and the trust in local partners and companies; and (4) split incentives and uncompensated benefts. These barriers have become even more signifcant following the COVID-19 pandemic due to an

**Fig. 10.1** Financing landscape in the EU for energy renovations according to market maturity and type. (Adapted from Economidou et al., 2019)


**Table 10.1** Deep renovation fnancing key terms and concepts

(*continued*)



(*continued*)



exceptional increase in governments' fscal defcits and the consequential decrease in governmental funds available for incentivising the transition to more energy-effcient buildings (Tian et al., 2022).

Traditional funding mechanisms (e.g., government grants and incentives, loans) have demonstrated that they cannot cope with the growing demand for and need of capital to fnance building renovation, so it is not surprising that the entire sector is constantly trying to attract more private investments (Tian et al., 2022). However, this is quite challenging given the scale of the investment required for these kinds of projects and the challenges associated with measuring the impact of "green" investments (United Nations, 2019).

Recent developments in the area of fnancial technologies (FinTech) have demonstrated how digital technologies can be leveraged to beneft both capital seekers (entrepreneurs, frms, and project promoters) and capital givers (investors), and therefore foster innovation in many sectors (Lynn & Rosati, 2021). FinTech can be seen as "a co-evolution and convergence of fnance and technology" (Lynn et al., 2019, p. V) where new service providers typically leverage customer-centric platform-based business models enabled by the Internet and different degrees of disintermediation to overcome the limitations of the traditional fnancial system in terms of supply and access to capital, and the barriers to entry typical of traditional capital markets (Tönnissen et al., 2020; Lynn & Rosati, 2021; Sánchez, 2022). In so doing, they provide both small retail investors and large institutional investors with access to new investment opportunities, and capital seekers with additional funding they would not receive otherwise. In fact, in many cases, projects that seek funding through these alternative channels do not meet the requirements of traditional fnancial institutions in terms of credit history or are at an early stage of development, and therefore are not attractive to venture capitalist or investment funds. As such, these alternative sources of fnance generate clear benefts not only for the parties involved in the transactions but for the economy as a whole (Sánchez, 2022).

While alternative sources of fnance that are enabled by FinTech have gained signifcant traction in many sectors, the construction sector is still lagging behind in terms of adoption and is still mostly reliant on debtbased solutions (Ziegler et al., 2020). This suggests that FinTech solutions may play a pivotal role in supporting building renovation and therefore contributing to the ambitious sustainability targets that have been set by the EU and the United Nations (Economidou et al., 2019; United Nations, 2019).

In this chapter we focus on two main alternative sources of fnance, namely crowdfunding, which is more established, and blockchain-based solutions, which are more novel and fast-growing.

#### 10.3 Crowdfunding for Building Renovation

Economidou et al. (2011) provide an early identifcation of equity and debt crowdfunding as new and innovative sources of fnancing for building renovations. Crowdfunding is ideal in the manner in which it circumvents the constraints that exist in traditional bank fnancing, providing instead a new marketplace that allows for the pooling of fnancing from many retail investors ("the crowd") to support the building renovation project (Kunkel, 2015). Panteli et al. (2020) note that communities can become shareholders in energy effciency projects through the mechanism of crowdfunding markets, allowing for greater buy-in from communities in the roll out of renewable energy and energy effciency initiatives. Crowdfunding markets provide fexibility through connecting investors and benefciaries directly, while offering lower costs of fnancing resulting from the use of the technology to facilitate the marketplace (Bertoldi et al., 2021). There are, of course, certain disadvantages to crowdfunding as articulated by Economidou et al. (2011): (a) the risk for benefciaries in not securing the required level of funding, and (b) and the risk for investors in assuming all of the associated risks with extending the fnancing. It is within the latter context that the marketplaces for crowdfunding are less regulated than traditional markets.

In terms of project scale, Panteli et al. (2020) position crowdfunding as an ideal source of private fnancing for small-scale energy upgrading. Crowdfunding has the potential to form an important part of the funding mix, along with private-public and fully public funding mechanisms. An identifed barrier to scaling up the amount of crowdfunding for building renovation is the wider public's understanding of crowdfunding markets.

Kunkel (2015) argues the merits for crowdfunding as a source of fnancing for building renovation as follows:


fnancial return and will be well placed to appraise the broader nonfnancial benefts, particularly in terms of the societal and environmental impact.

There are no studies, to the authors' knowledge, that empirically examine the crowdfunding of building renovation projects specifcally. There is a more established literature however, albeit somewhat limited, that has studied the crowdfunding of real estate and renewable energy projects, which provides some useful insights.

In the context of real estate investment, Montgomery et al. (2018) use Disruptive Innovation Theory as a setting to appraise the potential for crowdfunding to be a disruptive source of fnancing. Based on a systematic literature review, the authors provide arguments for real estate crowdfunding as a disruptive innovation. Real estate crowdfunding is identifed as offering cost and process effciencies through technological innovation, having lower performance in certain areas (e.g., cybersecurity risk) relative to conventional fnancing channels, creating and facilitating a new marketplace for fnancing, and having less appeal among mainstream large real estate developers, while appealing to existing and new small- to mediumsized real estate developers. Shahrokhi and Parhizgari (2019) underscore the disruptive nature of real estate crowdfunding with a comparison against traditional fnancing, emphasising how the emergence of specialised crowdfunding platforms has overcome the high barriers historically to investment in real estate. Indeed, the authors note the explosion of platforms over recent years and the step change in real estate crowdfunding in the US from \$1bn in 2009 to \$17bn in 2015. Mamonov et al. (2017) confrm that real estate ventures are by the far the most successful proportion of the equity crowdfunding market in the US, constituting approximately 51% of all ventures that reached their minimum capital commitment target in the 2013–2016 period.

Through an empirical analysis of real estate crowdfunding campaigns in Italy, Gigante and Cozzio (2021) are able to identify the important determinants of successful crowdfunding campaigns, where success is defned as achieving (or exceeding) the target funding amount. Leveraging potential determinants from the general crowdfunding literature, the authors focus on the funding type (lending or equity), the duration of the investment, the minimum investment level for investors, and the expected annual return on investment. Duration is found to be important in that the longer the project the more diffcult it is to secure the required funding. It is also found that higher expected returns attract investors and increase the chances of successfully securing the required funding. Borrero-Domínguez et al. (2020) conducted a similar study in the Spanish market. This study corroborates the fndings of Gigante and Cozzio (2021) in showing that longer projects are less successful in securing funding, while projects that offer higher expected return are more successful. The authors also show that buy-to-sell projects are less successful than development loan projects, while greater levels of risk act as a deterrent for investors and impeding funding success.

In terms of the performance of real estate investment via crowdfunding markets, Schweizer and Zhou (2017) provide evidence that equity-based projects offer better returns, while higher levels of leverage are also associated with better returns. Other characteristics that lead to higher returns include provision for later payments to investors and higher minimum investment amounts.

In respect of the energy effciency dimension to building renovation, it is worth exploring the literature that has examined the crowdfunding of renewable energy technology. Cumming et al. (2017), for instance, consider the determinants that drive crowdfunding. The authors show that price of oil is an important factor in determining the level of crowdfunding, with higher oil prices associated with a greater prevalence of crowdfunding directed at clean technology. The authors also show that the use of soft information (e.g., photos, video pitch, and text descriptions) is more prevalent in renewable energy-based crowdfunding campaigns and that this is used as a tactic to mitigate information asymmetry concerns for investors. It is shown further that the success of these crowdfunding campaigns is more sensitive to the use of soft information around the projects.

Slimane and Rousseau (2020), in a similar study, seek to identify the factors that can lead to a successful crowdfunding campaign. Financial characteristics of the renewable energy project are found to be important, including the interest rate applied, the funding amount requested, the size of the frm in question, and the overall fnancial performance of the frm. Non-fnancial characteristics such as age and gender of the entrepreneur, in addition to the size of their social network, are found to be relevant.

While such studies demonstrate that crowdfunding can be successful from the benefciaries' perspective, what is the impact of such crowdfunding? Appiah-Otoo et al. (2022) provide evidence to support the tangible impact that crowdfunding can have on renewable energy development. The authors demonstrate that on a cross-country basis a 1% increase in crowdfunding bolsters actual renewable energy generation by 0.35%. Indeed, the authors further show a very interesting bi-directional causal relationship between crowdfunding and renewable energy generation. This suggests that the development of crowdfunding markets helps to channel the fnancing to expand renewable energy generation, while the expansion of renewable energy generation helps to attract investors to crowdfunding markets who are looking for investment opportunities.

Of course, the above insights are on the demand side of crowdfunding (i.e., the benefciaries). One also needs to consider the supply side of crowdfunding (i.e., the investors). Understanding investor perceptions and behaviours is pivotal here. Bergmann et al.'s (2021) study, for example, is one such study that provides qualitative cross-country survey evidence that a signifcant majority of those surveyed have a strong awareness of the existence of crowdfunding markets, with almost half having invested in such marketplaces previously. A signifcant minority (~40%) indicated an intention to invest in renewable energy projects through crowdfunding channels over the next three years.

Literature also tells us that the platform has a central role to play in the successful mobilisation of crowdfunding to renewable energy projects. For example, De Broeck (2018) studies best practices in respect of platforms servicing investment in renewable energy projects. The qualitative analysis provides insights across a number of key dimensions of crowdfunding activity around renewable energy projects: the impact of regulation, risk exposures resulting from the underlying platform business models, and the platforms' attitude towards risk.

De Broeck (2018) fnds that crowdfunding activity around renewable energy projects is strongest in jurisdictions where there is strong policy support for renewable energy, citing premium tariffs and/or feed-intariffs, which offer better long-term certainty over the cash fows associated with the renewable energy projects. When assessing the platforms on the basis of credit risk, De Broeck (2018) is able to identify a set of platforms that work to a combination of low risk supports (such as feed-intariffs) and low risk instruments (secured business loans, bonds/ debentures, and senior bond loans), while another set of platforms works to a combination of very low risk tariff premiums and high risk instruments (subordinate proft participating loans). The presence of strong support is seen as an important measure for the mitigation of credit risk for investors, which encourages more crowdfunding activities. De Broeck (2018) also fnds that due diligence procedures are deemed to be the most signifcant measure that platforms can take to mitigate the credit risk exposure of investors. Platforms that reduce credit risk exposure ensure greater and more persistent levels of engagement from investors, protecting the resulting supply of funding to renewable energy projects.

## 10.4 Blockchain for Building Renovation

Blockchain technology was originally proposed in 2008 by Satoshi Nakamoto as the technology underpinning Bitcoin (Nakamoto, 2008). While most of the attention around blockchain was initially devoted to payment and other transactional systems, a number of alternative use cases across different industries have emerged over time. With a specifc focus on the built environment, for instance, Arup (2019) considers blockchain applications in the context of property, but also the wider and associated areas of smart cities, energy, transport, and water. Khatoon et al. (2019) note how blockchain is being considered in areas such as large-scale energy trading systems, peer-to-peer energy trading, project fnancing, supply chain tracking, and asset management. The focus of Khatoon et al. (2019) is on the application of blockchain in energy effciency, where they show that blockchain-based smart contracting provides a solution to effcient and transparent trading of energy effciency savings. Blockchain also offers potential for effcient building information management, with Liu et al. (2021) reviewing the literature towards addressing gaps in the smart city context.4 Woo et al. (2021) provide a similar review with specifc focus on building energy management. The remainder of this section focuses on three areas—energy performance contracting, building and renovation fnancing, and digital twinning.

We begin with energy performance contracting. An energy performance contract (EPC) is described as a creative fnancing mechanism that funds energy upgrades in, for example, building renovation works.5 The EPC involves a contract with an assigned energy services company (ESCO) that designs and delivers on the energy effciency plan, with the (future) revenues from the resulting costs savings being used to net off against the

<sup>4</sup>Relatedly, there is a literature that has considered the role that the Internet of Things can play in the real-time monitoring and management of building information. See, for example, Altohami et al. (2021) for a review.

<sup>5</sup> https://e3p.jrc.ec.europa.eu/articles/energy-performance-contracting#:~:text= Energy%20Performance%20Contracting%20(EPC)%20is,energy%20upgrades%20from%20 cost%20reductions.

(primarily upfront) expenses around the project. Aoun (2020) notes that EPCs are suitable when funding sources are elusive, maintenance is lacking, or new equipment and technology is needed and requires unique skills. The EPC area has been well studied for a considerable period of time; Zhang and Yuan (2019) provide a comprehensive review of recent literature.

Blockchain is of interest in the area of energy performance contracting as the technology offers scope to introduce effciencies into the process, while it also allows for trust to be established between the parties involved in the building renovation given the integrity of the blockchain. Schletz et al. (2020), for example, discuss how blockchain can provide an alternative channel through which to raise the required capital for the energy effciency plan underlying an EPC. This utilises the process of tokenisation. Engineering digital tokens for sale to investors over a blockchain allows a way to pool funding from a large array of both retail and institutional investors. This is effectively a crowdfunding market, akin to what we met previously, but rather than being based on traditional debt and equity instruments, it is based on digital tokens6 and fully decentralised. Schletz et al. (2020) propose the use of security tokens—which are more strongly regulated versions of digital tokens and which may refect more closely traditional debt and equity instruments—under such blockchain applications.7 Blockchain-based smart contracts then allow the automated transfer of the capital raised to the ESCO, while it also allows for income, as defned under the security token specifcation, to be transferred back to the investors. Aoun (2020) provides a wider discussion, proposing a blockchain model design suitable for energy performance contracting, which builds trust for the main players involved: customers, investors, and the ESCO. Exploitation of smart contracts is proposed for (1) the effcient recording of data collected from the implemented energy conservation measures, specifcally logging data (via oracle technology) from external sensors in a smart contract (data logger smart contract); (2) the calculation of the daily adjusted baseline energy consumption based on the logged data and some agreed formulation, and the calculation of the

<sup>6</sup>While a discussion of digital tokens is beyond the scope of this chapter, the interested reader is directed to, for example, Tasca (2019) for a review of token-based business models.

<sup>7</sup> Stekli and Cali (2020) also consider the potential of security tokens as an equity crowdfunding channel for offshore wind energy, while Halden et al. (2021) do similarly for solar energy.

actual daily savings achieved with reference to this baseline (adjustments smart contract); and (3) the incrementing of the monthly savings record with the calculated daily savings (savings smart contract). Gürcan et al. (2018) similarly consider how blockchain can potentially reconcile, in the case of energy performance contracting, the requirement to process and analyse large volumes of data and the requirement to implement complex algorithms to determine the baseline energy consumption against which the actual energy consumption is benchmarked.

Blockchain can, more generally, facilitate funding release in the real estate market. While the concept of real estate tokenisation is new, the market is developing and use cases are emerging. A widely referenced case is AspenCoin, the frst real estate Security Token Offering (STO). Launched in 2018, it raised US \$18 million within a 2-month period in exchange for 18.9% of the ownership of the St. Regis Aspen Resort in Aspen, Colorado (Carroll, 2018). Real estate tokenisation offers fractional ownership opportunities, widening the funding pool for real estate investments and creating liquid secondary real estate markets where the trading of real estate tokens can occur (Baum, 2021). In the context of commercial real estate, Smith et al. (2019) also emphasise the benefts of blockchain in terms of securitisation and trading, but extend the discussion to the potential application of blockchain to the real estate investment value chain and to the representation of the physical assets. Smart contracts are again core to these blockchain applications allowing for automation of processes. From an empirical perspective, Swinkels (2022) provides one of the frst studies of the real estate token market in the US, providing evidence that tokenisation is indeed leading to notable fractionalisation of ownership. Furthermore, Swinkels (2022) documents an alignment between the prices of real estate tokens and the US house price index, showing an integration of virtual and real property markets.

Finally, blockchain has considerable potential in the area of digital twinning. Hunhevicz et al. (2022) consider how blockchain can be integrated and exploited leveraging a blockchain-based business model that relies on interaction between the physical building environment and the virtual building environment. The latter serves to simplify the connection between the real world data and the smart contracts, reducing the data storage requirement of the smart contract. Similar to the previous studies, the blockchain is shown to be useful in delivering funding into the building project via digital tokenisation, and in the automated execution of the main phases of the energy performance contract via a smart contract, while it further allows for trust in the transactions between all parties involved.

#### 10.5 Conclusion

This chapter summarises the somewhat limited literature that exists addressing the intersection of the fnancial technology and the building renovation domains. This defcit of knowledge means that there is a tangible opportunity to advance research in the directions outlined in respect of non-blockchain-based crowdfunding and blockchain-based crowdfunding, although the latter will take some years for the required tokenbased marketplaces to emerge and mature. Given the EU's present focus on overhauling the existing Energy Effciency Directive towards achieving its ambitious building renovation targets, the potential for meaningful policy impact from timely research is pronounced.

From our discussion of non-blockchain-based crowdfunding, it is evident that there is a defcit of knowledge and empirical evidence in respect of the crowdfunding of building renovation. Little is known on the demand side (crowdfunding benefciaries) or the supply side (crowdfunding investors), or indeed on the responsibilities of crowdfunding platforms. The existing literature on crowdfunding for real estate investments and renewable energy projects literature provides some useful insights that are likely to be relevant in the building renovation space. However, dedicated empirical studies that track the crowdfunding directed at building renovation projects are required, while an understanding of whether and how crowdfunding platforms promote and support building renovation projects (relative to new building development projects) is needed in order to assess the funding landscape holistically in the context of the built environment. More insight is also required into customer views of crowdfunding as a channel to fnance building renovation. There are idiosyncratic features to building renovation that require more thoughtful consideration to appraise how crowdfunding can be optimised to deliver on the required scale of building renovation. In the case of the EU, such tailored research would have the potential to impact building renovation policy.

In respect of blockchain-based crowdfunding, the nascent nature of these market innovations means that time will reveal much information on the success of such blockchain applications. Future studies may attempt to answer the question: how can tokenisation most effectively work as a funding release mechanism (beyond energy performance contracting) for building renovation specifcally? Our exploration of blockchain in respect of energy performance contracting is clearly new and the literature sparse. As technical blockchain developments continue in practice, we will likely see the emergence of active token-based markets that will drive funding towards building renovation work. Similar to the knowledge gaps identifed in previous sections, empirical evidence will need to be accumulated in respect of the demand side (benefciaries) and the supply side (investors) of these token-based markets. What drives a successful Security Token Offering will be important to ascertain, while the comparison of such blockchain-based crowdfunding will need to be compared against existing non-blockchain-based equity and debt crowdfunding. Furthermore, as we see greater adoption of smart contracts in energy performance contracting, we will be able to appraise the effectiveness of the fnancing mechanism in terms of its return performance and risk profle.

#### References


**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/ by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Index1

#### **A**

Additive manufacturing, 16, 98–104, 119, 145 Architecture, Engineering, Construction, And Operations (AECO), 84, 86, 88, 89, 139 Artifcial intelligence (AI), 42, 46, 47, 56, 70, 72, 73, 90, 146 Augmented reality (AR), 90, 91

#### **B**

Big area additive manufacturing (BAAM), 100 Big Data, 16, 64, 70–78 Blockchain, 42, 43, 145, 164–168 Building information modelling (BIM), 12, 15, 28, 29, 40–47, 59, 63, 64, 72–75, 77, 78, 124, 144

Building Management Systems (BMS), 77 Building performance simulation (BPS), 15, 54–64, 78 Building simulation (BS), 54, 56–64

### **C**

CleanTech, 8 Climate change, 2, 8–11, 78 ClimateTech, 8 Cloud service provider (CSP), 32 Cobots, vii Co2 emissions, 73, 99, 100, 104 Crowdfunding, 17, 155, 159–165, 165n7, 167, 168 Cyber-physical systems, 43, 145 Cybersecurity, 17, 33, 40, 136–147, 161

1Note: Page numbers followed by 'n' refer to notes.

© The Author(s) 2023 T. Lynn et al. (eds.), *Disrupting Buildings*, Palgrave Studies in Digital Business & Enabling Technologies, https://doi.org/10.1007/978-3-031-32309-6

#### **D**

Decision support systems (DSS), 44, 56 Deep learning (DL), 72, 88, 89 Deep renovation, 2–17, 24, 31–33, 41, 43–47, 60, 64, 70–78, 84–92, 99–101, 136–147, 155–159 Denial of service, 137 Descriptive analytics, 71 Diagnostic analytics, 71 Digital twin, 16, 25, 28, 43, 84–92, 124 Digital twinning, 91, 164, 166 District heating systems, 4

#### **E**

Embedded sensors, 15, 24–33 Energy monitoring, 27, 55 Energy Performance Building Directive, 4 Energy performance certifcation, 2 Energy performance contracting (EPC), 164–166, 168 Energy Service Agreements (ESAs), 156 European Green Deal, 2 Exoskeletons, 114, 122, 145

**F** Fintech, 17, 155, 159

#### **G**

General Data Protection Regulation (GDPR), 32 Green mortgages, 157

#### **H**

Heating, Ventilation And Air Conditioning (HVAC), 3, 11, 30, 31, 47, 57, 59, 73, 78, 145

#### **I**

Identity theft, 137 Information and communications technology (ICT), 3 Intelligent equipment and robots (IER), 16, 17, 113–119, 122, 124–128 Internet Of Things (IoT), 13, 24, 27–31, 42, 70, 90, 137, 146, 164n4

### **L**

Leasing, 157 Loans, 158, 162, 163

#### **M**

Machine learning (ML), 16, 46, 47, 70–73, 84, 146

#### **N**

Near Zero Energy Building (nZEB), 24 Neural network, 72, 74, 88

### **P**

Passive House Standard, 12 Phishing, 137 Photovoltaic panels (PV panels), 3, 120, 121 Predictive analytics, 71 Preventive monitoring, 29 Principal component analysis (PCA), 87 Programmable Construction Machines (PCM), 116

#### **R**

Random sample consensus (RANSAC), 87

Ransomware, 137 Renewable energy sources (RES), 3, 78

#### **S**

Security Token Offering (STO), 166, 168 Smart buildings (SB), 15, 24, 26–27, 30–32, 84, 145 Smart contract, 165–168 Smart homes, 15, 26–27, 33

#### **T**

3D printing, 16, 99, 100, 102–104 3D scanning, 16

Tokenisation, 17, 165, 166, 168

#### **U**

Unmanned aerial vehicle (UAV), 119, 120, 124, 145

#### **V**

Virtual reality (VR), 55, 90, 91 Visual analytics, 74

#### **W**

Waste management, 72 Wire arc additive manufacturing (WAAM), 100