Feedback on a shared big dataset for intelligent TBM PartⅠ:Feature extraction and machine learning methods  

作　　者：Jian-Bin Li Zu-Yu Chen Xu Li Liu-Jie Jing Yun-Pei Zhangf Hao-Han Xiao Shuang-Jing Wang Wen-Kun Yang Lei-Jie Wu Peng-Yu Li Hai-Bo Li Min Yao Li-Tao Fan 

机构地区：[1]China Railway Group Co.Ltd.,Beijing 100089,China [2]Department of Geotechnical Engineering,China Institute of Water Resources and Hydropower Research,Beijing 100038,China [3]Key Laboratory of Urban Underground Engineering,Ministry of Education,Beijing Jiaotong University,Beijing 100044,China [4]China Railway Engineering Equipment Group Co.,Ltd.,Zhengzhou,Henan 450016,China [5]School of Civil Engineering,Southeast University,Nanjing 211189,China [6]Xi’an University of Technology,Xi’an 710048,China

出　　处：《Underground Space》2023年第4期1-25,共25页地下空间（英文）

基　　金：supported by the National Key R&D Program of China(Grant No.2018YFB1702504);the National Natural Science Foundation of China(Grant Nos.52179121,51879284);the State Key Laboratory of Simulations and Regulation of Water Cycle in River Basin,China(Grant No.SKL2022ZD05);the IWHR Research&Development Support Program,China(Grant No.GE0145B012021);the Natural Science Foundation of Shaanxi Province,China(Grant No.2021JLM-50);the National Key R&D Program of China(Grant No.2022YFE0200400).

摘　　要：This review summarizes the research outcomes and findings documented in 45 journal papers using a shared tunnel boring machine(TBM)dataset for performance prediction and boring efficiency optimization using machine learning methods.The big dataset was col-lected during the Yinsong water diversion project construction in China,covering the tunnel excavation of a 20 km-section with 199 items of monitoring metrics taken with an interval of one second.The research papers were the result of a call for contributions during a TBM machine learning contest in 2019 and covered a variety of topics related to the intelligent construction of TBM.This review com-prises two parts.Part I is concerned with the data processing,feature extraction,and machine learning methods applied by the contrib-utors.The review finds that the data-driven and knowledge-driven approaches in extracting important features applied by various authors are diversified,requiring further studies to achieve commonly accepted criteria.The techniques for cleaning and amending the raw data adopted by the contributors were summarized,indicating some highlights such as the importance of sufficiently high fre-quency of data acquisition(higher than 1 second),classification and standardization for the data preprocessing process,and the appro-priate selections of features in a boring cycle.The review finds that both supervised and unsupervised machine learning methods have been utilized by various researchers.The ensemble and deep learning methods have found wide applications.Part I highlights the impor-tant features of the individual methods applied by the contributors,including the structures of the algorithm,selection of hyperparam-eters,and model validation approaches.

关 键 词：Big data Machine learning method TBM construction Data extraction Machine learning contest 

分 类 号：U45[建筑科学—桥梁与隧道工程]