Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data

doi:10.1016/j.tust.2020.103595

	Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data
	Liu, Quansheng 1,2; Wang, Xinyu 1,2; Huang, Xing 3; Yin, Xin 1,2
刊名	TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
	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
DOI	10.1016/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
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000591661300003
内容类型	期刊论文
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.