Enhancing ground loss rate prediction in soft-soil shield tunneling: a synergistic approach of peck back analysis and eXtreme Gradient Boosting and bayesian optimization.

This study investigates the prediction of ground loss rate during soft-soil shield tunneling using Peck's back analysis method and XGBoost model. Bayesian optimization is employed to determine optimal hyperparameters, ensuring comprehensive and efficient model tuning. The XGBoost model is compared with Random Forest (RF) and Support Vector Machine (SVM) models to benchmark its performance. The results demonstrate the superior accuracy and robustness of the XGBoost model. Also, the results show that the soil properties and the grouting factors of the excavation face affect the duration of the instantaneous settlement of the ground surface. There is a specific correlation between the depth-to-diameter ratio, the coefficient of variation in the advancing speed of the shield machine, the maximum surface subsidence, and the ground loss rate. The prediction model of the ground loss rate based on the combined approach of Peck back analysis and eXtreme Gradient Boosting and Bayesian optimization has high reliability in soft-soil layers, and this method can provide a specific reference for predicting construction risk in related projects.