Abstract
Frequent collapse accidents in tunnels are associated with many construction risk factors, and the interrelationship among these risk factors is complex and ambiguous. This study’s aim is to clarify the relationship among risk factors to reduce the tunnel collapse risk. A multicriteria decision-making method is proposed by combining interpretive structural modeling (ISM) and fuzzy Bayesian network (FBN). ISM is used to determine the hierarchical relationships among risk factors. FBN quantitatively analyzes the strength of the interaction among risk factors and conducts risk analysis. The ISM-FBN method contains three steps: (1) drawing the ISM-directed graph; (2) obtaining the probability of the FBN nodes; and (3) using GeNle to implement risk analysis. The proposed method is also used to assess the collapse risk and detect the critical factors in the Canglongxia Tunnel, China. This method’s tunnel collapse risk model can provide managers with clear risk information and better realize project management.
Highlights
In recent years, with the continuous increase in traffic demand and the shortage of land, the tunnel relies on shortening the road mileage, relieving ground transportation pressure, and improving city operation efficiency; the advantages of saving land and protecting the environment have been rapidly developed
Methodology e Interpretive structural modeling (ISM)-fuzzy Bayesian network (FBN) method is developed to improve the tunnel collapse risk assessment’s accuracy and establish and quantify the interaction among the collapse factors. e workflow is shown in Figure 3, which includes three main parts: (i) drawing the ISM-directed graph; (ii) obtaining Bayesian network (BN) probability; and (iii) risk analysis
We investigated five experts to obtain the probability of BN nodes. e specific steps are as follows: (1) obtaining the natural language value of the root nodes through experts; (2) converting natural language values into fuzzy sets; (3) aggregating expert opinions by similarity aggregation method (SAM); (4) obtaining the node probability value using equations (14) and (15); and (5) obtaining the conditional probability of other nodes following the aforementioned steps
Summary
With the continuous increase in traffic demand and the shortage of land, the tunnel relies on shortening the road mileage, relieving ground transportation pressure, and improving city operation efficiency; the advantages of saving land and protecting the environment have been rapidly developed. During the tunnel’s construction, the complex engineering-geological environment has intense uncertainty, leading to tunnel construction safety accidents. Common tunnel construction safety accidents include collapse, water and mud inrush, gas explosion, rock burst, large deformation, fire, etc. If these accidents are not adequately controlled, the project’s construction progress will be affected, and severe injuries and property losses will be caused, which will cause a tremendous social impact. Among the 111 tunnel construction safety accidents counted from 2001 to 2016, tunnel collapse accidents accounted for about 56% [1]; this type of accident had aroused widespread public concern and increased the sensitivity to the risk of tunnel collapse
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