Use of soft computing techniques for tunneling optimization of tunnel boring machines

作     者：Isam Shahrour Wengang Zhang 

作者机构：College of Civil EngineeringTongji UniversityShanghai 200092China Laboratoire Genie Civil et geo-EnvironnementUniversite de LilleFrance School of Civil EngineeringChongqing UniversityChina 

出 版 物：《Underground Space》 (地下空间（英文）)

年 卷 期：2021年第6卷第3期

页      面：233-239页

核心收录：

学科分类：081406[工学-桥梁与隧道工程] 08[工学] 0814[工学-土木工程] 082301[工学-道路与铁道工程] 0823[工学-交通运输工程] 

主　　题：Soft computing Tunneling Tunnel boring machine Artificial neural network Machine learning Optimization Settlement Convergence Artificial intelligence 

摘      要：Thanks to advances in tunnel boring machine(TBM)and monitoring,significant progress has been achieved in the application of soft computing techniques for the optimization of TBM tunneling and the reduction of disturbance related to tunneling in urban *** experimental,analytical,and numerical methods have limitations in solving problems related to TBM tunneling,engineers can use soft computing techniques in analyzing the relationship between the target tunneling responses and influential design inputs parameters,including the geometrical,geological,and TBM operational *** techniques are useful in achieving robust and low-cost ***,engineers face difficulties in making an optimal choice of the soft computing technique to solve the complex problems related to TBM *** help with this choice,this study presents state of the art about the use of soft computing techniques in TBM tunneling through practical *** study proposes recommendations for the optimal use of these techniques,in particular(i)the importance of preliminary analyses for the selection and reduction of input parameters,(ii)the necessity to complete insufficient data using laboratory tests and numerical modeling,(iii)the selection of reduced number of hidden layers and nodes to avoid overfitting,(iv)the use of recurrent neural networks to deal with time-series data,and(v)the association of soft computing methods with hybrid optimization techniques to reduce the risk of convergence to local minima.