A Failure Probability Evaluation Method for the Collapse of Drill-Blast Tunnels Based on a Multistate Cloud Bayesian Network - Meng, Guowang; Liu, Jialiang; Qiu, Weixing; Wu, Bo; Xu, Shixiang (Frontiers in Earth Science, 2022)
Collapse is one of the main dangers of tunnel construction using the drill-blast method. To assess the risk of collapse and provide a basis for risk control, a failure probability evaluation method for tunnel collapse based on a Bayesian network (BN) and normal cloud theory is proposed in this paper. First, typical tunnel collapse cases are analysed statistically based on the risk breakdown structure method, a Bayesian network model is built for drill-blast tunnel collapse and the causal relationships between the tunnel collapse and influential factors, such as geological factors and construction management factors, are revealed. Second, the multiple fault states of the nodes, including the surrounding rock grade and groundwater, are described using fuzzy numbers. Normal cloud theory is utilized to describe the failure probability of the nodes, and the uncertain logic relationship between nodes described by a multistate fuzzy conditional probability table is established. Finally, the construction safety analysis progress is extended to the entire life cycle of risk-prone events, including pre-accident, during-construction dynamic evaluation, and post-accident control. A typical hazard concerning tunnel collapse in the construction of the Fujian Jinzhupa tunnel in China is presented as a case study to verify the applicability of the proposed approach. The results prove the feasibility of the method and its application potential. A comparison of advantages and disadvantages between a cloud Bayesian network and other commonly used risk analysis tools is also conducted. The proposed method can be used to guide the safety analysis and management of construction projects, thereby increasing the probability of successful projects in complex environments.