Data-driven joint multi-objective prediction and optimization for advanced control during tunnel construction

This research develops a hybrid approach that integrates light gradient boosting machine (LightGBM) and nondominated sorting genetic algorithm II (NSGA-II) to optimize the tunnel boring machine (TBM) performance during excavation. The TBM operational data are first extracted and meta-models are established to estimate the key TBM performance, including the penetration rate, over/under excavation ratio, and energy consumption. An optimization process is proposed by adopting NSGA-II and the technique for order preference by similarity to an ideal solution (TOPSIS) analysis to determine ideal operational parameters. The developed approach acts as a useful tool that assists tunnel construction automation and improves TBM performance under different in-situ conditions. Real data from a tunnel project in Singapore is utilized as a case study to examine the applicability and efficiency of the proposed approach. The results indicate that (1) The proposed meta-model provides reliable estimation with an average RMSE and MAE of 2.604mm/min and 3.402mm/min for TBM's penetration rate(O1), 0.0211 and 0.0324 for over/under excavation ratio (O2), and 15.512kwh and 23.088kwh for energy consumption (O3), respectively. The prediction accuracy is better than the state-of-the-art methods; (2) The TBM's performance can be optimized by the proposed approach with an average improvement of 33.14 %, 1.32 %, and 17.95 % for O1 to O3, respectively, and an overall improvement of 39.60 %; (3) The overall reliability of TBM operation improved after optimization with a significant reduction in data variance by 91.16 %, 76.92 %, and 97.35 % for O1 to O3, respectively. This paper contributes to proposing a novel method that integrates LightGBM with NSGA-II in resolving the complex TBM operation problem by considering the major performance indexes including excavation efficiency, safety, and energy consumption during TBM operation.