Predicting existing tunnel deformation from adjacent foundation pit construction using hybrid machine learning.

To accurately predict the existing tunnel deformation from adjacent foundation pit construction (AFPC), a hybrid prediction framework based on random forest recursive feature elimination and the Bayesian optimization natural gradient boosting algorithm (RF-RFE-BO-NGBoost) is presented in this paper. The key findings from this study include the following: (1) RF-RFE effectively screens out crucial parameters, with the optimal feature subset postscreening encompassing ten influencing factors. (2) The BO-NGBoost-based prediction model for existing tunnel deformation from AFPC achieves high accuracy, with R2 values ranging from 0.914 to 0.935, RMSE values ranging from 0.104 to 0.364, MAE values ranging from 0.089 to 0.335, and MAPE values ranging from 3.08% to 10.71% (3) SHapley Additive ExPlanations (SHAP) determines the contribution of each parameter, identifying important construction parameters influencing existing tunnel deformation. The hybrid prediction framework proposed herein provides guidance for realizing the excavation safety management of existing tunnels. • A method for predicting the adjacent existing tunnel deformation caused by foundation pit construction is proposed. • RF-RFE is used to screen out important parameters and BO-NGBoost is used to predict existing tunnel deformation. • The contribution of each parameter to the model output is analyzed using SHAP. • A tunnel construction example in China is taken as an example for verification. [ABSTRACT FROM AUTHOR]