Development of data-driven predictive model and enhanced multiobjective optimization to improve the excavation performance of large-diameter slurry shields

Abstract

Safety, efficiency and energy consumption are important aspects for evaluating the performance of large-diameter slurry shield, and improving the performance of shield is crucial for safe and efficient excavation. To this end, a data-driven hybrid method is developed to improve the excavation performance of large-diameter slurry shields by intelligence regulating shield parameters. This method combines Bayesian Optimization with categorical boosting (BO-CatBoost) and enhanced multiobjective evolutionary algorithm based on decomposition (EMOEA/D). The method uses surface settlement, penetration and specific energy as output targets and employs the expert knowledge to select the input parameters. Subsequently, the trained BO-CatBoost model is employed to fit the input-output relationship. On this basis, the multiobjective optimization process was performed using EMOEA/D, with the important parameters determined by Shapley Additive exPlanations as decision variables and the nonlinear relationship fitted by BO-CatBoost as the objective function. Finally, the technique for order preference similarity to ideal solution is applied to obtain optimal operational parameters, thereby enhancing the excavation performance of large-diameter slurry shield. The proposed method is applied to a Wuhan rail transit line to verify the effectiveness, and the result shows that: (1) Our method can accurately predict the three targets with goodness of fit ranging from 0.938 to 0.988, respectively. (2) The proposed method can effectively improve the excavation performance of the large-diameter slurry shield, and reaches 13.88 %, 5.21 %, and 10.88 %, respectively. (3) An adaptive decision-making system for setting operational parameters is constructed, which is valuable for formulating of operational control strategies for large-diameter slurry shields.