基于多态模糊贝叶斯网络的地铁车站深基坑坍塌可能性评价

Evaluation of collapse possibility of deep foundation pits in metro stations based on multi-state fuzzy Bayesian networks

针对地铁车站深基坑施工风险因素多、坍塌事故多发,且传统方法在多态性以及精确概率难以获取的复杂系统风险分析方面存在一定的局限性,提出了一种基于多态模糊贝叶斯网络的深基坑坍塌可能性评价方法。该方法根据建立的风险分析故障树来构建多态贝叶斯网络模型;利用模糊数描述根节点的故障状态和发生概率,克服了不能考虑事件中间故障状态对系统的影响以及精确概率难以获取的问题。基于贝叶斯网络正向推理实现了用根节点的模糊概率和施工中实际故障状态两种不同的方式计算基坑坍塌风险发生的概率,从而可以实现基坑施工过程中的实时动态风险分析,而且可以根据敏感性分析结果辨识关键致险因子以指导风险控制工作,同时可以通过反向推理获得各根节点的后验概率以进行故障诊断及进一步预测系统状态。实例分析表明:该方法能够科学、合理地评价基坑坍塌风险并确定关键致险因子,可作为基坑施工安全风险管理的决策工具。

Many construction risk factors and frequent collapse accidents are associated with deep foundation pits in metro stations, and there are limitations for the traditional methods to conduct risk analysis of such complex systems with multiple states. In this study, a method for evaluation of collapse possibility of deep foundation pit collapse based on multi-state fuzzy Bayesian network is proposed. The multi-state Bayesian network model was constructed via fault tree transformation, and fuzzy numbers were used to describe the fault state and the failure rate of root nodes, which overcomes the problem that the traditional methods cannot consider the influence of intermediate fault states and are difficult to obtain the accurate failure rate. Based on the forward reasoning of Bayesian network, the risk probability of foundation pit collapse can be calculated in two different ways including the fuzzy probability of root nodes and the actual fault state in construction. As a result, a real-time dynamic risk analysis during foundation pit construction can be achieved. Furthermore, the key risk factors can be identified for the guidance of risk control according to the sensitivity analysis results. In addition, the posterior probability of each root node can be obtained by backward reasoning to carry out fault diagnosis and further predict the system state. Two case studies show that the proposed method can scientifically and reasonably evaluate the collapse risk of foundation pit and determine the key risk factors, which can be used as a decision-making tool for safety risk management of foundation pit construction.