Predictive model for assessing the nonlinear surface displacement and mechanical response of shallowly buried tunnels under dip-slip fault dislocation

作     者：Wang, Mingnian Yang, Henghong Yu, Li Zhang, Xiao 

作者机构：Southwest Jiaotong Univ Sch Civil Engn Chengdu 610036 Peoples R China Southwest Jiaotong Univ State Key Lab Intelligent Geotech & Tunnelling Chengdu 610036 Peoples R China 

出 版 物：《SOIL DYNAMICS AND EARTHQUAKE ENGINEERING》 (Soil Dyn. Earthqu. Eng.)

年 卷 期：2025年第191卷

核心收录：

学科分类：07[理学] 0708[理学-地球物理学] 0818[工学-地质资源与地质工程] 

基　　金：National Natural Science Foundation of China Fundamental Research Funds for the Central Universities [2682024CX020] China National Railway Group Limited Science and Technology Research and Development Program [K2023G041] China Postdoctoral Science Foundation [2023M742898] Postdoctoral Fellowship Program of CPSF [GZC20232193] Natural Science Foundation of Sichuan [24NSFSC7133, 2025ZNSFSC1295] Technology Program of Chengdu [TICSTR-2022-IV-002-1] 52378411 

主　　题：Dip-slip faults Tunnel engineering Predictive model Tunnel response Nonlinear surface displacement 

摘      要：The fault dislocation induces permanent ground surface displacement, leading to severe damage to tunnels. However, there is a notable scarcity of predictive models for nonlinear surface displacement and tunnel response under dip-slip fault dislocation. Previous analytical models have oversimplified surface displacement to a constant value. To this end, first, a predictive model for assessing the mechanical response of shallowly buried tunnels under dip-slip fault dislocation is established. Then, through a mathematical statistical analysis of field- measured data, a prediction method of nonlinear dip-slip surface displacement has been developed, and the nonlinear dip-slip surface displacement is introduced into the predictive model. The predictive model incorporates nonlinear surface displacement, fault zone width, and geometric nonlinearity, thereby markedly enhancing the accuracy of the calculation results. Secondly, the prediction model undergoes validation through experimental tests and numerical simulations, revealing a maximum error of 3.7 %. In contrast, neglecting nonlinear surface displacement can result in calculation errors as high as 517.5 %. Finally, the proposed predictive model is applied to conduct a parameter analysis, such as maximum surface displacement (Delta dmax), dip angle (alpha), and fault zone width (WF). The results shown that the maximum axial force (Nmax), maximum shear force (Vzmax), and maximum bending moment (Mzmax) of the tunnel increase with the augmentation of Delta dmax. For each incremental increase of 0.2 m in Delta dmax, the Nmax, Vzmax, and Mzmax exhibit an approximate increase of 22.1 %-100.3 %. The Nmax and decreases with the increasing alpha, whereas both the Vzmax and the Mzmax increase as alpha rises. With each incremental increase of 10 degrees in alpha, the Nmax diminishes by approximately 16.1 %-49.2 %, while both the Vzmax and Mzmax experience an increase ranging from about 4.6 % to 19.1 %. An increase in WF results i