Improved unascertained measure model for risk evaluation of collapse in highway tunnels

Xiaoduo Ou, Yufang Wu, Bo Wu (), Jie Jiang (), Jingyi Chen and Lu Zhang
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Xiaoduo Ou: Guangxi University
Yufang Wu: Guangxi University
Bo Wu: East China University of Technology
Jie Jiang: Guangxi University
Jingyi Chen: Guangxi University of Finance and Economics
Lu Zhang: Guilin University of Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 119, issue 3, No 1, 1149-1170

Abstract: Abstract Tunnel collapse is a serious hazard that poses a substantial threat to worker safety and construction expenses. The results of risk evaluations can provide valuable information for collapse prevention and risk management. For this purpose, an improved unascertained measure function for collapse risk evaluation was proposed. Firstly, a risk evaluation index system was established using major indexes, including geological conditions, design and construction factors. Secondly, a combination method of the analytic hierarchy process (AHP) and the anti-entropy weight (AEW) based on the maximum deviation (MD) concept was proposed. The subjective and objective weights were linearly optimized to assign the index weights. Finally, the conventional linear unascertained measure function was improved by normalizing. By combining the weights, the values of the evaluation indexes were regarded as intervals for calculating the measure values. Confidence recognition analysis was used to determine the collapse risk level. Jinzhupa Tunnel was evaluated to have a high collapse risk and experienced a small-scale collapse during excavation, verifying the feasibility of the improved unascertained measure model. This method considers the uncertainty of the evaluation index and can effectively predict the risk level of tunnel collapse.

Keywords: Highway tunnels; Risk evaluation; Anti-entropy weight; Maximum deviation; Unascertained measure theory (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-06116-5

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