Tunnel collapse risk assessment based on multistate fuzzy Bayesian networks

Abstract

Excessive structural deformation or collapse can lead to heavy casualties and substantial property loss. This paper presents a novel integrated risk assessment method based on multistate fuzzy Bayesian networks integrated with historical data, expert investigations, probability distribution calculations, discrepancy analysis, sensitivity analysis, and decision‐making. A new expert investigation is proposed with small probability intervals, expert weights, confidence index, etc. After gaining expert judgment by expert investigation, Chauvenet's criterion is first introduced in a discrepancy analysis to eliminate outlier data from the expert judgment and obtain a more reliable value. The t distribution and its confidence interval are also adopted to determine the characteristic value of the survey data as a triangular fuzzy number. A conditional probability table of the model is integrated with historical data and prior knowledge through the weight index. Sensitivity analysis is used to identify the critical factors by changing the probability distribution of each factor and observing the related changes in the risk event. The proposed method ensures the accuracy and scientific rigor of the assessment and the diagnosis of a tunnel accident. This method is successfully applied to assess the collapse probability of the Yu Liao Tunnel.

Ignorance‐aware safety and reliability analysis: A heuristic approach

“…For example, Burke et al proposed a novel design selection strategy using multiple criteria with enhanced robustness of user priorities . Sun et al introduced a novel expert investigation with consideration of small probability intervals in order to assess the risk of tunnel collapse . A risk matrix is developed using an extension TOPSIS method and an intuitionistic fuzzy number by Yazdi to enhance the performance of a safety system in a refinery under construction .…”

“…22 Sun et al introduced a novel expert investigation with consideration of small probability intervals in order to assess the risk of tunnel collapse. 23 A risk matrix is developed using an extension TOPSIS method and an intuitionistic fuzzy number by Yazdi to enhance the performance of a safety system in a refinery under construction. 3 In the group decision-making procedure, each expert is asked to express their opinions about which grade level (eg, possibility and priority) belongs to a specified factor (eg, alternative and event), and subsequently, the membership function can be obtained by further analyzing all decision-makers' opinions.…”

Assessment of risks of tunneling project in Iran using artificial bee colony algorithm

“…The results showed that the risk of swelling of rock had the highest rank [26]. Sun et al [27] investigated tunnel collapse risk using multistate fuzzy Bayesian networks. The results showed that this method was very suitable for evaluating of the collapse probability of the Yu Liao Tunnel [27].…”

“…Sun et al [27] investigated tunnel collapse risk using multistate fuzzy Bayesian networks. The results showed that this method was very suitable for evaluating of the collapse probability of the Yu Liao Tunnel [27]. Xiong et al [28] proposed a method for 3D multi-scale geology modeling for tunnel engineering risk assessment.…”

A Comprehensive Assessment Method for Safety Risk of Gas Tunnel Construction Based on Fuzzy Bayesian Network

“…Bayesian networks can be used to solve the practical problems based on the case [9][10][11][12]. Conditional probability tables have been established by expert investigation with fuzzy Bayesian networks [13][14]. In addition, some assessment methods are applied during the mine gas disaster research process [15][16][17][18].…”