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oapen-20.500.12657-890562024-04-03T02:22:58Z Chapter Extracting Information from Construction Safety Requirements Using Large Language Model Khan, Nasrullah Kimito, Emmanuel Charles Tran, Si Pedro, Akeem Soltani, Mehrtash Hussain, Rahat Yoo, Taehan Park, Chansik Construction safety document extraction LLM thema EDItEUR::U Computing and Information Technology The 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 2024-04-02T15:45:04Z 2024-04-02T15:45:04Z 2023 chapter ONIX_20240402_9791221502893_25 2704-5846 9791221502893 https://library.oapen.org/handle/20.500.12657/89056 eng Proceedings e report application/pdf n/a 9791221502893_76.pdf https://books.fupress.com/doi/capitoli/979-12-215-0289-3_76 Firenze University Press 10.36253/979-12-215-0289-3.76 10.36253/979-12-215-0289-3.76 bf65d21a-78e5-4ba2-983a-dbfa90962870 9791221502893 137 7 Florence open access
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The 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
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