Abstract
Flood has become the main hidden threat to the safety of the karst tunnel. Scientific and reasonable flood classification assessment and prevention measures have also become the main problems to be solved urgently in the construction and operation of tunnels. Aiming at the problems of multiple influencing factors, complex index systems, and linear processing of the connection degree of set pair analysis, a set pair cloud model based on cloud theory eigenvalues is proposed to optimize the connection degree of set pair analysis. Regarding the uncertainty of the evaluation system and the ambiguity of the evaluation grade boundary, firstly, the ridge-type membership function is used to determine the basic probability distribution of each measured index. Secondly, the distance discrimination weighting theory based on the index distance is also established, so that each measured index can assign the dynamic weight of the evaluation system. It can comprehensively determine the weight of the cloud connection degree for the system, and draw the cloud level diagram to determine the tunnel flood risk level. Finally, the proposed model is verified with tunnel flood. The results show that the evaluation results are consistent with other evaluation models and actual tunnel conditions. This model can provide guidance for flood prediction and prevention in karst tunnels.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant no. 52011530394, 52020105002); Featured Innovation Project of General Universities in Guangdong Province (Grant no. 2021KTSCX221); Basic and Applied Basic Research Project of Guangzhou Basic Research Program (Grant no. 202201010859).
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Jiang, Y., Cui, J. & Zhang, Y. Risk Assessment Mmodel of Karst Tunnel Flood Based on Distance Discriminant Weighting and Set Pair Cloud. KSCE J Civ Eng 27, 3219–3229 (2023). https://doi.org/10.1007/s12205-023-2319-7
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DOI: https://doi.org/10.1007/s12205-023-2319-7