气候变化领域集成服务门户(CCPortal): Back-analysis for initial ground stress field at a diamond mine using machine learning approaches

CCPortal

DOI	10.1007/s11069-020-04304-1
	Back-analysis for initial ground stress field at a diamond mine using machine learning approaches
	Pu Y.; Apel D.B.; Prusek S.; Walentek A.; Cichy T.
发表日期	2021
ISSN	0921030X
起始页码	191
结束页码	203
卷号	105 期号:1
英文摘要	Exact knowledge for ground stress field guarantees the construction of various underground engineering projects as well as prediction of some geological hazards such as the rock burst. Limited by costs, field measurement for initial ground stresses can be only conducted on several measure points, which necessitates back-analysis for initial stresses from limited field measurement data. This paper employed a multioutput decision tree regressor (DTR) to model the relationship between initial ground stress field and its impact factor. A full-scale finite element model was built and computed to gain 400 training samples for DTR using a submodeling strategy. The results showed that correlation coefficient r between field measurement values and back-analysis values reached 0.92, which proved the success of DTR. A neural network was employed to store the global initial ground stress field. More than 600,000 node data extracted from the full-scale finite element model were used to train this neural network. After training, the stresses on any location can be investigated by inputting corresponding coordinates into this neural network. © 2020, Springer Nature B.V.
关键词	Feed-forward neural network Full-scale finite element model Initial ground stress field Multioutput decision tree regressor
英文关键词	artificial neural network; back analysis; diamond; finite element method; geological hazard; machine learning; mine; rockburst; stress field
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206507
作者单位	State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing, China; School of Mining and Petroleum Engineering, University of Alberta, Edmonton, Canada; Central Mining Institute (GIG), Katowice, Poland
推荐引用方式 GB/T 7714	Pu Y.,Apel D.B.,Prusek S.,et al. Back-analysis for initial ground stress field at a diamond mine using machine learning approaches[J],2021,105(1).
APA	Pu Y.,Apel D.B.,Prusek S.,Walentek A.,&Cichy T..(2021).Back-analysis for initial ground stress field at a diamond mine using machine learning approaches.Natural Hazards,105(1).
MLA	Pu Y.,et al."Back-analysis for initial ground stress field at a diamond mine using machine learning approaches".Natural Hazards 105.1(2021).