Research on the causes of earthwork foundation pit collapse based on Fault tree and Bayesian network

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dc.contributor.author Shi, Donghui
dc.contributor.author Gan, Shuling
dc.contributor.author Zurada, Jozef
dc.contributor.author Feilong, Wang
dc.contributor.author Wang, Yuanyuan
dc.contributor.author Guan, Jian
dc.date.accessioned 2023-12-26T18:36:41Z
dc.date.available 2023-12-26T18:36:41Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other ab3758c9-9095-4842-8eab-c6967d5bae57
dc.identifier.uri https://hdl.handle.net/10125/106506
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Science and Machine Learning to Support Business Decisions
dc.subject bayesian network
dc.subject earthwork foundation pit collapse
dc.subject text mining
dc.subject the fault tree
dc.title Research on the causes of earthwork foundation pit collapse based on Fault tree and Bayesian network
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract With the rapid development of the construction industry, construction safety accidents frequently occur. Among these accidents, earthwork foundation pit collapse, as a subset of construction accidents, often causes significant casualties and economic losses due to the self-weight of collapsed materials and the extensive area affected. The purpose of this study is to identify the causes of construction safety and collapse accidents and their relationships in order to facilitate effective supervision and prevention during the construction process. Firstly, text mining is conducted on historical construction safety accident reports using R language tools and the TF-IDF algorithm to obtain keywords related to accident causative factors. Then the risk factors are analyzed to establish the basic event, intermediate event, and top event of the accident, to construct a fault tree of casualties caused by earthwork foundation pit collapse accidents and analyze the structural importance of risk factors. Finally, the fault tree is transformed to a Bayesian network using image mapping and numerical mapping to enable the analysis of node sensitivity and the prediction of top event probability to provide scientific reference for safety accident prediction and prevention.
dcterms.extent 10 pages
prism.startingpage 1070
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