中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data

文献类型:期刊论文

作者Liu, Quansheng1,2; Wang, Xinyu1,2; Huang, Xing3; Yin, Xin1,2
刊名TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
出版日期2020-12-01
卷号106页码:13
关键词TBM Rock mass classification Integrated algorithm AdaBoost-CART model SMOTE
ISSN号0886-7798
DOI10.1016/j.tust.2020.103595
英文摘要The real-time acquisition of surrounding rock information is important for the efficient tunneling and hazard prevention in tunnel boring machines (TBMs). This study presents an ensemble learning model based on classification and regression tree (CART) and AdaBoost algorithm to predict the classification of surrounding rock mass. Statistical indicators (i.e., mean value and standard deviation) of TBM operational parameters were calculated and used as input variables, and the rock mass classification obtained by the hydropower classification (HC) method was used as output variable. To develop the model, a database was established, consisting of 3166 samples collected from the Songhua River Water Conveyance Tunnel. The synthetic minority over-sampling technique (SMOTE) was utilized to address the imbalance ratio of rock mass classifications in the database. The results of the testing set showed that the accuracy and F1-measure of AdaBoost-CART were 0.865 and 0.770, respectively, which are better than the results of the standard CART (0.753 and 0.629, respectively). The application of SMOTE improves the recall of minority classes. Compared with artificial neural networks, k-nearest neighbor, and support vector classifier, the developed AdaBoost-CART model achieves better performance. The variable importance was analyzed to distinguish key features; the results showed that rock mass boreability may not be a major consideration of the HC method. The presented model can provide significant guidance for the real-time acquisition of surrounding rock information during TBM tunneling.
资助项目major special project of the National Natural Science Foundation of China[41941018]
WOS研究方向Construction & Building Technology ; Engineering
语种英语
WOS记录号WOS:000591661300003
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.198/handle/2S6PX9GI/25140]  
专题中科院武汉岩土力学所
通讯作者Huang, Xing
作者单位1.Wuhan Univ, Sch Civil Engn, Key Lab Geotech & Struct Engn Safety Hubei Prov, Wuhan 430072, Hubei, Peoples R China
2.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Hubei, Peoples R China
3.Chinese Acad Sci, State Key Lab Geomech & Geotech Engn, Inst Rock & Soil Mech, Wuhan 430071, Hubei, Peoples R China
推荐引用方式
GB/T 7714
Liu, Quansheng,Wang, Xinyu,Huang, Xing,et al. Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data[J]. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,2020,106:13.
APA Liu, Quansheng,Wang, Xinyu,Huang, Xing,&Yin, Xin.(2020).Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data.TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,106,13.
MLA Liu, Quansheng,et al."Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data".TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 106(2020):13.

入库方式: OAI收割

来源:武汉岩土力学研究所

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