Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning

doi:10.1016/j.tust.2021.104285

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	Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning
	Yin, Xin 1,2; Liu, Quansheng 1,2; Huang, Xing 3; Pan, Yucong 1,2
	2022-02-01
发表期刊	TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
ISSN	0886-7798
卷号	120 页码:20
摘要	The perception of surrounding rock geological conditions ahead the tunnel face is essential for TBM safe and efficient tunnelling. This paper developed a perception approach of surrounding rock class based on TBM operational big data and combined unsupervised-supervised learning. In data preprocessing, four data mining techniques (i.e., Z-score, K-NN, Kalman filtering, and wavelet packet decomposition) were used to detect outliers, substitute outliers, suppress noise, and extract features, respectively. Then, GMM was used to revise the original surrounding rock class through clustering TBM load parameters and performance parameters in view of the shortcomings of the HC method in the TBM-excavated tunnel. After that, five various ensemble learning classification models were constructed to identify the surrounding rock class, in which model hyper-parameters were automatically tuned by Bayes optimization. In order to evaluate model performance, balanced accuracy, Kappa, F-1-score, and training time were taken into account, and a novel multi-metric comprehensive ranking system was designed. Engineering application results indicated that LightGBM achieved the most superior performance with the highest comprehensive score of 6.9066, followed by GBDT (5.9228), XGBoost (5.4964), RF (3.7581), and AdaBoost (0.9946). Through the weighted purity reduction algorithm, the contributions of input features on the five models were quantitatively analyzed. Finally, the impact of class imbalance on model performance was discussed using the ADASYN algorithm, showing that eliminating class imbalance can further improve the model's perception ability.
关键词	TBM Surrounding rock class Perception model Unsupervised learning Supervised learning
DOI	10.1016/j.tust.2021.104285
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[41941018] ; National Natural Science Foundation of China[52074258] ; National Natural Science Foundation of China[42177140] ; National Natural Science Foundation of China[41807250] ; Key Research and Development Project of Hubei Province[2021BCA133]
WOS研究方向	Construction & Building Technology ; Engineering
WOS类目	Construction & Building Technology ; Engineering, Civil
WOS记录号	WOS:000765018400003
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：29[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.198/handle/2S6PX9GI/34697
专题	中科院武汉岩土力学所
通讯作者	Liu, Quansheng
作者单位	1.Wuhan Univ, Sch Civil Engn, Key Lab Geotech & Struct Engn Safety Hubei Prov, Wuhan 430072, Peoples R China 2.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China 3.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China
推荐引用方式 GB/T 7714	Yin, Xin,Liu, Quansheng,Huang, Xing,et al. Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning[J]. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,2022,120:20.
APA	Yin, Xin,Liu, Quansheng,Huang, Xing,&Pan, Yucong.(2022).Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning.TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,120,20.
MLA	Yin, Xin,et al."Perception model of surrounding rock geological conditions based on TBM operational big data and combined unsupervised-supervised learning".TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 120(2022):20.

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