DOI: 10.1007/s11069-020-04304-1
论文题名: Back-analysis for initial ground stress field at a diamond mine using machine learning approaches
作者: Pu Y. ; Apel D.B. ; Prusek S. ; Walentek A. ; Cichy T.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 105, 期: 1 起始页码: 191
结束页码: 203
语种: 英语
中文关键词: 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
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. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/169306
Appears in Collections: 气候变化与战略
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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
Recommended Citation:
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]. Natural Hazards,2021-01-01,105(1)