Data-driven joint multiobjective prediction and optimization for tunnel-induced adjacent bridge pier displacement: A case study in China

To reduce the impact of tunnel construction on adjacent bridge pile foundations and ensure safety during construction, a hybrid intelligent framework combining Bayesian optimization (BO), categorical boosting (CatBoost), and the nondominated sorting genetic algorithm-III (NSGA-III) is proposed in this paper. The nonlinear mapping function relationship between the nine input parameters and the bridge pier vertical and horizontal displacements is established via BO-CatBoost. The key optimization parameters are for interpretability analysis and determined via Shapley additive explanations (SHAP) method. NSGA-III is established with the goal of minimizing pier displacement. The applicability and validity of the proposed method are tested in a case study of the Wuhan Metro. The key findings of this study include the following. (1) The accuracy of the prediction model obtained by the BO-CatBoost algorithm on the basis of the training and simulation of the measured engineering data is high. On the bridge pier horizontal and vertical displacement test sets, the R² values are 0.823 and 0.826, the RMSE values are 0.452 and 0.539, and the MAEs are 0.293 and 0.360, respectively. (2) The optimization effect of the two objectives is significant, and the average percentage of improvement stands at 35.54%. When five shield construction parameters are adjusted simultaneously, the optimization effect of the two objectives is the best, and the average improvement percentage is 54.76%. (3) The optimization effect of the developed BO-CatBoost-NSGA-III intelligent algorithm is greater than that of single-objective optimization. Therefore, the intelligent optimization framework proposed in this paper can provide guidance for the optimal control of pier displacement in shield underpass construction engineering.