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dc.contributor.authorKhan, Nasrullah
dc.contributor.authorKimito, Emmanuel Charles
dc.contributor.authorTran, Si
dc.contributor.authorPedro, Akeem
dc.contributor.authorSoltani, Mehrtash
dc.contributor.authorHussain, Rahat
dc.contributor.authorYoo, Taehan
dc.contributor.authorPark, Chansik
dc.date.accessioned2024-04-02T15:45:04Z
dc.date.available2024-04-02T15:45:04Z
dc.date.issued2023
dc.identifierONIX_20240402_9791221502893_25
dc.identifier.issn2704-5846
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/89056
dc.description.abstractThe construction industry has long been recognized for its complex safety regulations, which are essential to ensure the well-being of on-site employees. However, navigating these regulations and ensuring compliance can be challenging due to the volume and complexity of the documents involved. This study proposes a novel approach to extracting information from construction safety documents utilizing Large Language Models (LLM), called CSQA, to provide real-time, precise answers to queries related to safety regulations. The approach comprises three modules: (1) the construction safety investigation module (CSI) collects safety regulations for building the information needed. By leveraging a collection of safety regulation PDFs, the system follows a process of text extraction, preprocessing, and global indexing for efficient search. (2) The safety condition identification module (SCI) retrieves the CSI database; after that, the LLM, with its extensive training, processes user queries, searches the indexed regulations, and retrieves pertinent information. (3) the safety information delivery (SID) would provide the answer to the user and incorporate a feedback mechanism to further refine system accuracy based on user responses. Preliminary evaluations reveal the system's superior performance over traditional search engines, owing to its ability to grasp query context and nuances. The CSQA presents a promising method for accessing safety regulations, with potential benefits including reduced non-compliance incidents, enhanced worker safety, and streamlined regulatory consultations in construction
dc.languageEnglish
dc.relation.ispartofseriesProceedings e report
dc.subject.classificationthema EDItEUR::U Computing and Information Technology
dc.subject.otherConstruction safety document
dc.subject.otherextraction
dc.subject.otherLLM
dc.titleChapter Extracting Information from Construction Safety Requirements Using Large Language Model
dc.typechapter
oapen.identifier.doi10.36253/979-12-215-0289-3.76
oapen.relation.isPublishedBybf65d21a-78e5-4ba2-983a-dbfa90962870
oapen.relation.isbn9791221502893
oapen.series.number137
oapen.pages7
oapen.place.publicationFlorence


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