Integration of FEM and DL for seismic performance prediction and optimization design of tunnels

Abstract

To address the challenge of the complex and extensive seismic design elements of tunnels, which are difficult to be accurately described using mathematical functions, a novel model combining convolutional neural networks (CNN), gated recurrent units (GRU), and an attention mechanism is proposed. Firstly, based on actual engineering examples, the tunnel dimensions and site soil information are determined to establish a numerical model of tunnel seismic response and verify its reliability. Then, the soil parameters, seismic motion amplitude, tunnel depth, and overlying water depth are selected for systematic analysis of the displacement momentum (DM) and time of maximum damage occurrence (TMDO). The parameters with higher influence are chosen as input variables, while the calculated DM and TMDO from the reliable numerical model are selected as the output variables to be predicted. Next, integrating the GRU model to capture long-term dependencies in time series, the CNN model to extract spatial features, and the attention mechanism to handle complex relationships among multiple variables, the CNN-GRU-Attention prediction model was established. By generating dataset samples through numerical simulation, accurate predictions of DM and TMDO were achieved. Finally, using the proposed model to establish the objective function relationship between input and output parameters, employing the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to find the optimal input design features, achieving the optimal design of tunnel seismic performance. The results show that: (1) The calculation results of the numerical model for tunnel seismic response conform to general research findings, indicating sufficient reliability. (2) The error compensation and dynamic updating mechanisms improved prediction accuracy. The R² values for the training set reach 0.973 and 0.982 respectively. (3) Optimizing DM and TMDO using the NSGA-II algorithm leads to a 23.42% reduction in DM and a 18.71% increase in TMDO. After optimization, tunnel displacement is reduced, damage is delayed, and seismic performance is significantly improved.