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A platform for research: civil engineering, architecture and urbanism
Multi-status Bayesian network for analyzing collapse risk of excavation construction
https://orcid.org/0000-0001-5209-5169
https://orcid.org/0000-0002-5610-7988
Abstract In response to the need for effective collapse risk analysis in engineering projects, a decision support approach is presented. It is rooted in multi-status Bayesian network (MSBN) and fuzzy set theory, encompassing MSBN construction, risk analysis, and management. This research addresses the causative correlation between influential factors and excavation collapse, considering the inherent uncertainty and fuzziness of the project. To enhance the reliability of evaluation results, an expert confidence index, integrating judgment ability, subjective reliability, and risk preference, is introduced. The approach is applied to an excavation case study, demonstrating its viability and capacity. Furthermore, it offers actionable insights for decision-makers to proactively mitigate accident probabilities. Notably, sensitivity analysis identifies critical risk factors. This research contributes to the field of engineering project risk assessment and management. It serves as a foundation for future research and development, guiding the way for improved strategies and decision support systems.
Graphical abstract Display Omitted
Highlights Multi-status Bayesian network (MSBN) integrated with fuzzy set theory for analyzing collapse risk is developed. Cause effect correlation relationship between factors and collapse risk of engineering project is evaluated via MSBN. Fuzzy set theory was utilized to quantity fuzziness in collected data. Capacity and feasibility of approach is illustrated via a real case project. It provides a decision tool for engineering project with insufficient measured data.
Multi-status Bayesian network for analyzing collapse risk of excavation construction
https://orcid.org/0000-0001-5209-5169
https://orcid.org/0000-0002-5610-7988
Abstract In response to the need for effective collapse risk analysis in engineering projects, a decision support approach is presented. It is rooted in multi-status Bayesian network (MSBN) and fuzzy set theory, encompassing MSBN construction, risk analysis, and management. This research addresses the causative correlation between influential factors and excavation collapse, considering the inherent uncertainty and fuzziness of the project. To enhance the reliability of evaluation results, an expert confidence index, integrating judgment ability, subjective reliability, and risk preference, is introduced. The approach is applied to an excavation case study, demonstrating its viability and capacity. Furthermore, it offers actionable insights for decision-makers to proactively mitigate accident probabilities. Notably, sensitivity analysis identifies critical risk factors. This research contributes to the field of engineering project risk assessment and management. It serves as a foundation for future research and development, guiding the way for improved strategies and decision support systems.
Graphical abstract Display Omitted
Highlights Multi-status Bayesian network (MSBN) integrated with fuzzy set theory for analyzing collapse risk is developed. Cause effect correlation relationship between factors and collapse risk of engineering project is evaluated via MSBN. Fuzzy set theory was utilized to quantity fuzziness in collected data. Capacity and feasibility of approach is illustrated via a real case project. It provides a decision tool for engineering project with insufficient measured data.
Multi-status Bayesian network for analyzing collapse risk of excavation construction
https://orcid.org/0000-0001-5209-5169
https://orcid.org/0000-0002-5610-7988
Abstract In response to the need for effective collapse risk analysis in engineering projects, a decision support approach is presented. It is rooted in multi-status Bayesian network (MSBN) and fuzzy set theory, encompassing MSBN construction, risk analysis, and management. This research addresses the causative correlation between influential factors and excavation collapse, considering the inherent uncertainty and fuzziness of the project. To enhance the reliability of evaluation results, an expert confidence index, integrating judgment ability, subjective reliability, and risk preference, is introduced. The approach is applied to an excavation case study, demonstrating its viability and capacity. Furthermore, it offers actionable insights for decision-makers to proactively mitigate accident probabilities. Notably, sensitivity analysis identifies critical risk factors. This research contributes to the field of engineering project risk assessment and management. It serves as a foundation for future research and development, guiding the way for improved strategies and decision support systems.
Graphical abstract Display Omitted
Highlights Multi-status Bayesian network (MSBN) integrated with fuzzy set theory for analyzing collapse risk is developed. Cause effect correlation relationship between factors and collapse risk of engineering project is evaluated via MSBN. Fuzzy set theory was utilized to quantity fuzziness in collected data. Capacity and feasibility of approach is illustrated via a real case project. It provides a decision tool for engineering project with insufficient measured data.
Multi-status Bayesian network for analyzing collapse risk of excavation construction
Lin, Song-Shun (author) / Zhou, Annan (author) / Shen, Shui-Long (author)
2023-11-08
Article (Journal)
Electronic Resource
English
Excavation engineering , Multi-status Bayesian network , Fuzzy set theory , Risk analysis , Decision- making , Analytical hierarchy process , AHP , Bayesian network , BN , Cause-effect correlation relationship , CECR , Fuzzy risk prior probability , FRPP , MSBN , Multi-status conditional probability table , MSCPT , Prior probability , PP , Risk failure probability , RFP , Triangular fuzzy possibly number , TFPNs , Triangular fuzzy possibly sets , TFPS
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