Multi-objective optimization of shield construction parameters based on random forests and NSGA-II. (October 2022)

• Record Type: Journal Article
• Authors: Wu, Xianguo; Wang, Lei; Chen, Bin; Feng, Zongbao; Qin, YaWei; Liu, Qiong; Liu, Yang
• Is Part Of: Advanced engineering informatics. Volume 54(2022)
• Publication Date: 2022-10
• Subjects: Shield construction parameters -- Surface settlement -- Driving speed -- Multiobjective optimization -- RF-NSGA-II; SS Surface settlement -- SCP Shield construction parameters -- DS Driving speed -- RF Random forest -- NGSA-II Non-dominant classification genetic algorithm II -- BPNN Back-propagation neural network -- SVM Support vector machine -- GBDT Gradient boosting decision tree -- ANNs Artificial neural networks -- WPT Wavelet packet transform -- LSSVM Least-squares support vector machine -- GP Gaussian process -- MOEA Multiobjective evolutionary algorithm -- SPEA-II Strength Pareto evolutionary algorithm II; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101751
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP.