%0 Journal Article %T 基于BTM-BN的深大基坑事故链生机理研究及重大风险研判
Study on Accident Chain Mechanisms and Risk Assessment for Deep Foundation Pits Based on BTM-BN %A 田丝雨 %A 安宏斌 %A 郭健 %A 黄鑫元 %A 王阳 %A 魏思维 %J Hans Journal of Civil Engineering %P 554-569 %@ 2326-3466 %D 2025 %I Hans Publishing %R 10.12677/hjce.2025.143061 %X 随着城市地下空间开发利用规模的不断扩大，交通管网和城市管线的密度显著增加，环境条件复杂性增加与施工规模扩大，使基坑施工面临更高的技术和管理挑战。事故致险因素复杂多样，事故链机理尚不清晰，导致基坑施工重大安全事故频发；事故链生成规律的解析仍显不足，研究多以定性分析为主，难以满足地下工程建设的安全防控需求。本文对近十年基坑重大事故进行统计及特征分析，通过蝴蝶结模型，对基坑重大事故的机理进行研究；采用贝叶斯网络对致险因素进行量化分析，计算事故发生概率。结果表明，通过机理研究和重大风险研判揭示事故链生规律，并有效量化各致险因素对整体风险的影响，能够显著提升施工安全管理的效率，有助于减少事故损失，降低经济成本，同时为复杂地下工程的安全设计与施工提供了重要参考价值。
With the continuous expansion of urban underground space development, the density of transportation networks and pipelines has significantly increased. The increasing complexity of environmental conditions has brought technical challenges. The expansion of construction scales has added management difficulties to foundation pit construction. Accident-inducing factors are highly diverse, and the mechanisms of accident chains remain unclear, leading to frequent major safety accidents in foundation pit projects. The analysis of accident chain generation patterns has shown limitations. Most studies have relied on qualitative methods, failing to meet the safety prevention and control needs of underground construction. In this study, major foundation pit accidents from the past decade were statistically analyzed, and their characteristics were examined. Using the bow-tie model, the mechanisms of major foundation pit accidents were studied. The Bayesian network was applied to quantify the influence of causal factors and calculate accident probabilities. The results revealed that the study of mechanisms and risk assessments clarified accident chain generation patterns and effectively quantified the impact of each causal factor on overall risk. This approach significantly improved the efficiency of safety management, reduced accident-related losses, and lowered economic costs. Additionally, it provided valuable reference data for the safe design and construction of complex underground projects. %K 深大基坑， %K 风险研判， %K 链生机理， %K 蝴蝶结模型， %K 贝叶斯网络
Deep Foundation Pit %K Risk Assessment %K Chain Mechanism %K Bow-Tie Model %K Bayesian Network %U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=110543