In view of the shortcomings in the risk assessment of deep-buried tunnels, a dynamic risk assessment method based on a Bayesian network is proposed. According to case statistics, a total of 12 specific risk rating factors are obtained and divided into three types: objective factors, subjective factors, and monitoring factors. The grading criteria of the risk rating factors are determined, and a dynamic risk rating system is established. A Bayesian network based on this system is constructed by expert knowledge and historical data. The nodes in the Bayesian network are in one-to-one correspondence with the three types of influencing factors, and the probability distribution is determined. Posterior probabilistic and sensitivity analyses are carried out, and the results show that the main influencing factors obtained by the two methods are basically the same. The constructed dynamic risk assessment model is most affected by the objective factor rating and monitoring factor rating, followed by the subjective factor rating. The dynamic risk rating is mainly affected by the surrounding rock level among the objective factors, construction management among the subjective factors, and arch crown convergence and side wall displacement among the monitoring factors. The dynamic risk assessment method based on the Bayesian network is applied to the No. 3 inclined shaft of the Humaling tunnel. According to the adjustment of the monitoring data and geological conditions, the dynamic risk rating probability of level I greatly decreased from 81.7% to 33.8%, the probability of level II significantly increased from 12.3% to 34.0%, and the probability of level III increased from 5.95% to 32.2%, which indicates that the risk level has risen sharply. The results show that this method can effectively predict the risk level during tunnel construction.

中文翻译：

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基于贝叶斯网络的深埋隧道动态风险评估方法

针对深埋隧道风险评估存在的不足，提出了一种基于贝叶斯网络的动态风险评估方法。根据案例统计，共得到12个特定风险评级因素，分为客观因素、主观因素和监测因素三类。确定风险评级因素的分级标准，建立动态风险评级体系。基于该系统的贝叶斯网络由专家知识和历史数据构建而成。贝叶斯网络中的节点与三类影响因素一一对应，确定概率分布。进行后验概率和敏感性分析，结果表明，两种方法得到的主要影响因素基本一致。所构建的动态风险评估模型受客观因素评级和监测因素评级的影响最大，其次是主观因素评级。动态风险等级主要受客观因素中围岩水平的影响，主观因素中主要受施工管理的影响，监测因素中主要受拱冠收敛和边墙位移的影响。将基于贝叶斯网络的动态风险评估方法应用于呼马岭隧道3号斜井。根据监测数据和地质条件的调整，一级动态风险评级概率从81.7%大幅下降到33.8%，II级的概率从12.3%显着上升到34.0%，III级的概率从5.95%上升到32.2%，表明风险水平急剧上升。结果表明，该方法能有效预测隧道施工过程中的风险等级。