Affiliation:
1. School of Energy and Power Engineering, Xihua University, Chengdu 610039, China
2. Key Laboratory of Fluid Machinery and Engineering, Xihua University, Chengdu 610039, China
3. Key Laboratory of Fluid and Power Machinery, Xihua University, Ministry of Education, Chengdu 610039, China
Abstract
Water inrush in karst tunnels will cause casualties and economic losses. Thus, it is significant to objectively assess the water inrush risk level and adopt valid preventive measures to reduce losses from this disaster. The relationship between the factors affecting water inrush in the dynamic coupling system is strong nonlinear, so the attribute recognition model, which lessens the mutation points and error and causes the evaluation results to be more reasonable and accurate, is improved nonlinearly in this paper. Firstly, the assessment system was established by selecting seven factors: formation lithology, unfavorable geological conditions, attitude of rock formation, landform and physiognomy, contact zones of dissolvable and insoluble rock, layer and interlayer fissures, and groundwater level. Secondly, the multi-factor interaction matrix, C-OWA operator, and variable weight theory are used to calculate the constant weight and variable weight of each evaluation index. In addition, the linear attribute measurement function of the attribute identification model is optimized by using the non-linear trigonometric function to distinguish the risk level of the water inrush. Finally, the proposed model was successfully used in Qiyueshan Tunnel. The evaluation results of the risk level are more accurate than other methods, and they are in agreement with the excavation results. The proposed model provides a valuable reference for the risk assessment and project management of tunnel construction.
Funder
Natural Science Foundation of Sichuan Province
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science