Risk assessment of collapse in mountain tunnels and software development

Abstract

As most frequent geological hazard, the collapse is a great threat to safe construction of tunnels. The risk assessment method for collapse in mountain tunnels was presented with six evaluation indices, including surrounding rock level I 1 , rock mass integrity I 2 , buried depth I 3 , bias angle I 4 , groundwater I 5 , and construction factors I 6 . According to the non-linear relationship between the collapse and its influencing factors, the synthetic attribute assessment model was improved based on the concept of normal distribution. The weight of each index consisted of two parts: the subjective weight obtained by analytic hierarchy process (AHP) and the objective weight determined by correlation analysis. The Mountain Tunnel Collapse Risk Assessment System (TCAS) was developed based on Visual Studio 2012 and C# programming language. The TCAS was applied to assess the collapse risk of Hongyansi tunnel from Baoyi to Yichang expressway (Xiangyang section). The result was a good agreement with actual excavation situation that a "closed door" collapse happened. Therefore, the software system can not only accurately assess the risk level of collapse but also will play an important role in rapid assessment in the whole process of tunnel construction.

A dynamic assessment method on collapse risk of small spacing tunnels based on attribute interval recognition model

“…In recent years, many experts and scholars have proposed a variety of systematic evaluation methods to evaluate the tunneling collapse risk. The Mountain Tunnel Collapse Risk Assessment System was developed based on Visual Studio 2012 and C# programming language and was applied to assess the collapse risk of Hongyansi tunnel (Wang et al 2020a). Based on kinematical approach and power-law failure criterion, the basic expressions of collapse shape and region were deduced for the highway tunnels in soils taking deep buried highway tunnel with large section as objective (Yang et al 2014).…”