Pipes and tunnels are prone to longitudinal deformation under normal faulting. Predicting the active length, defined as the major deformed length, is crucial for the seismic design of pipes and tunnels. However, an accurate prediction method is hard to develop due to a limited number of experimental data. In this study, a large number of numerical simulations are conducted based on a three-dimensional beam-spring model validated by centrifuge tests. The results are fed to XGBoost models to develop a robust prediction method. For ease of application, only the friction angle of soil, burial depth, diameter, and thickness of pipes or tunnels are incorporated as features. A hyperparameter tuning approach integrating grid search and Bayesian optimization was employed in the training process to establish optimal models with comparatively low complexity and high accuracy. A comparison of predictions from the XGBoost models and curves fitted on relative structure-soil stiffness demonstrates that XGBoost models are much superior. The effects of each feature on the predictions were analyzed by employing the SHAP method. The proposed XGBoost models can effectively and efficiently predict the active length of pipes and tunnels with minimal inputs.

来源平台：COMPUTERS AND GEOTECHNICS