Machine Learning-Based Prediction of External Pressure in High-Speed Rail Tunnels: Model Optimization and Comparison

Xiazhou She, Yongxing Jia (), Rui Li, Jianlin Xu, Yonggang Yang, Weiqiang Cao, Lei Xiao and Wenhao Zhao
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Xiazhou She: School of Mechanical and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Yongxing Jia: School of Mechanical and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Rui Li: School of Mechanical and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Jianlin Xu: School of Mechanical and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Yonggang Yang: School of Mechanical and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Weiqiang Cao: School of Mechanical and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Lei Xiao: School of Mechanical and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Wenhao Zhao: School of Mechanical and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Forecasting, 2025, vol. 7, issue 3, 1-24

Abstract: The pressure fluctuations generated during high-speed train passage through tunnels can compromise both the train’s structural integrity and passenger comfort, highlighting the need for the accurate prediction of external pressure wave amplitudes. To address the high computational cost of multi-condition Computational Fluid Dynamics simulations, this study proposes a hybrid method combining numerical simulation and machine learning. A dataset was generated using simulations with five input features: tunnel length, train length, train speed, blockage ratio, and measurement point location. Four machine learning models—random forest, support vector regression, Extreme Gradient Boosting, and Multilayer Perceptron (MLP)—were evaluated, with the MLP model showing the highest baseline accuracy. To further improve performance, six metaheuristic algorithms were applied to optimize the MLP model, among which, the sparrow search algorithm (SSA) achieved the highest accuracy, with R 2 = 0.993, MAPE = 0.052, and RMSE = 0.112. A SHapley Additive exPlanations (SHAP) analysis indicated that the train speed and the blockage ratio were the most influential features. This study provides an effective and interpretable method for pressure wave prediction in tunnel environments and demonstrates the first integration of SSA optimization into aerodynamic pressure modeling.

Keywords: high-speed train; pressure fluctuation; machine learning; metaheuristic algorithm; model interpretability (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
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