Abstract
Ground collapse is easily induced during tunnel construction in karst terrain and seriously threatens the safety of humans and surface and underground facilities. To implement measures to prevent the occurrence of ground collapse during tunnel construction, it is essential to assess the risk of ground collapse before tunnel construction. In this paper, the extension evaluation method was improved to establish a system for risk assessment of ground collapse along tunnels in karst terrain. The development process of the above risk assessment system mainly included the establishment of an evaluation index system, nondimensionalization of evaluation indices, matter-element establishment for ground collapse, weight determination for evaluation indices, correlation analysis and recognition of the ground collapse risk. To increase the reliability of the risk assessment results, the original extension evaluation method was improved in two aspects. First, to eliminate the impact of the dimensions of evaluation indices on the evaluation process and achieve comparison between different evaluation indices, the nondimensionalization process was applied to the adopted evaluation indices. Second, to simultaneously consider the theoretical cognition of humans and factual information of evaluation objects, the comprehensive weighting method combining the fuzzy analytical hierarchy process and correlation analysis was applied to determine the weights of the evaluation indices. The feasibility and effectiveness of the proposed assessment system were verified based on a case study. The advantages of the improved extension evaluation method over other approaches and the accuracy enhancement of the assessment results due to the improvements in the evaluation method were proven through comparative analysis. In addition, engineering countermeasures were proposed to prevent ground collapse according to the obtained risk assessment results. The improved extension evaluation system proposed in this paper could provide a valuable tool to predict the ground collapse risk before tunnel construction.
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