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 le...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.展开更多
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)+3 种基金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).
文摘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.
