The application of reinforcement learning to NATM tunnel design

作     者：Enrico Soranzo Carlotta Guardiani Wei Wu 

作者机构：University of Natural Resources and Life SciencesFeistmantelstraße 4Vienna 1180Austria 

出 版 物：《Underground Space》 (地下空间（英文）)

年 卷 期：2022年第7卷第6期

页      面：990-1002页

核心收录：

学科分类：08[工学] 0814[工学-土木工程] 

基　　金：provided by the Otto Pregl Foundation for Geotechnical Fundamental Research 

主　　题：Deep Q-Network NATM Reinforcement learning Support classes Tunnelling 

摘      要：The New Austrian Tunnelling Method(NATM)tunnel design is performed by testing support classes against the geological profile.We propose to replace this manual process with reinforcement learning,a generic framework within the realm of artificial intelligence that solves control tasks.Previous studies have demonstrated this possibility,albeit with methodological simplifications.We coupled the Finite Difference Method with a Python script,used the output of the first to train the machine learning model implemented in the latter and improved the choice of the support classes.Through benchmark tests,we demonstrated that our method was capable of choosing the optimal support classes for various geological sets and showed the relation between its performance and the number of training episodes.