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

被引:53
作者
Li, Jian-Bin [1 ]
Chen, Zu-Yu [2 ]
Li, Xu [3 ]
Jing, Liu-Jie [4 ]
Zhangf, Yun-Pei [2 ]
Xiao, Hao-Han [2 ]
Wang, Shuang-Jing [4 ]
Yang, Wen-Kun [5 ]
Wu, Lei-Jie [3 ]
Li, Peng -Yu [4 ]
Li, Hai -Bo [3 ]
Yao, Min [3 ]
Fan, Li -Tao [6 ]
机构
[1] China Railway Grp Co Ltd, Beijing 100089, Peoples R China
[2] China Inst Water Resources & Hydropower Res, Dept Geotech Engn, Beijing 100038, Peoples R China
[3] Beijing Jiaotong Univ, Key Lab Urban Underground Engn, Minist Educ, Beijing 100044, Peoples R China
[4] China Railway Engn Equipment Grp Co Ltd, Zhengzhou 450016, Henan, Peoples R China
[5] Southeast Univ, Sch Civil Engn, Nanjing 211189, Peoples R China
[6] Xian Univ Technol, Xian 710048, Peoples R China
基金
国家重点研发计划;
关键词
Big data; Machine learning method; TBM construction; Data extraction; Machine learning contest; IMBALANCED DATA; PREDICTION; CLASSIFICATION; PERFORMANCE; ROCK; TESTS; SMOTE;
D O I
10.1016/j.undsp.2023.01.001
中图分类号
TU [建筑科学];
学科分类号
0813 ;
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.
引用
收藏
页码:1 / 25
页数:25
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