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        Chapter Safety Training for Rigging Using Virtual Reality

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        Author(s)
        Lemouchi, Rafik
        Assaf, Mohamed
        Al-Hussein, Mohamed
        Boutouhami, Khaoula
        Bouferguene, Ahmed
        Language
        English
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        Abstract
        Tower and Mobile Cranes are some of the most commonly used heavy equipment in all construction sites, and any crane failures could lead to significant human and monetary losses. Moreover, rigging configuration determination is a critical task that requires the rigging crew to have significant experience and knowledge of various failure modes that can be encountered when performing lifting operations. However, despite the criticality of training riggers, there has yet to be a comprehensive tool used to train and guide inexperienced riggers, and hence, more practical tools are needed. This paper proposes a framework for using Virtual reality (VR) and simulation to train riggers to identify the optimal rigging configurations based on the lift type and the external conditions. Through 3D modeling, the critical components of the rigging system are modeled to accurately simulate the rigging system and their performance when faced with critical loading scenarios. The developed framework is expected to allow inexperienced riggers to identify critical failure modes and enhance construction operations' overall safety performance and productivity. Furthermore, several scenarios are assessed based on historical evidence for rigging configuration failures, and the efficiency of the training tool is assessed through real-life scenarios and tests
        URI
        https://library.oapen.org/handle/20.500.12657/89114
        Keywords
        Crane Operations; Lift Planning; Rigging; Safety; Training; Virtual Reality
        DOI
        10.36253/979-12-215-0289-3.18
        ISBN
        9791221502893, 9791221502893
        Publisher
        Firenze University Press
        Publisher website
        https://www.fupress.com/
        Publication date and place
        Florence, 2023
        Series
        Proceedings e report, 137
        Classification
        Virtualization
        Pages
        11
        Rights
        https://creativecommons.org/licenses/by-nc/4.0/legalcode
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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