A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model

doi:10.3390/su11113212

CORC > 武汉岩土力学研究所 > 中科院武汉岩土力学所 > 岩土力学所知识全产出 > 期刊论文

	A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model
	Feng, Guangliang ; Xia, Guoqing ; Chen, Bingrui ; Xiao, Yaxun ; Zhou, Ruichen
刊名	SUSTAINABILITY
	2019
卷号	11 期号:11 页码:-
关键词	energy rockburst prediction microseismicity probabilistic neural network Jinping II hydropower station
ISSN号	2071-1050
DOI	10.3390/su11113212
英文摘要	Hydropower is one of the most important renewable energy sources. However, the safe construction of hydropower stations is seriously affected by disasters like rockburst, which, in turn, restricts the sustainable development of hydropower energy. In this paper, a method for rockburst prediction in the deep tunnels of hydropower stations based on the use of real-time microseismic (MS) monitoring information and an optimized probabilistic neural network (PNN) model is proposed. The model consists of the mean impact value algorithm (MIVA), the modified firefly algorithm (MFA), and PNN (MIVA-MFA-PNN model). The MIVA is used to reduce the interference from redundant information in the multiple MS parameters in the input layer of the PNN. The MFA is used to optimize the parameter smoothing factor in the PNN and reduce the error caused by artificial determination. Three improvements are made in the MFA compared to the standard firefly algorithm. The proposed rockburst prediction method is tested by 93 rockburst cases with different intensities that occurred in parts of the deep diversion and drainage tunnels of the Jinping II hydropower station, China (with a maximum depth of 2525 m). The results show that the rates of correct rockburst prediction of the test samples and learning samples are 100% and 86.75%, respectively. However, when a common PNN model combined with monitored microseismicity is used, the related rates are only 80.0% and 61.45%, respectively. The proposed method can provide a reference for rockburst prediction in MS monitored deep tunnels of hydropower projects.
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
语种	英语
WOS记录号	WOS:000472632200217
内容类型	期刊论文
源URL	[http://119.78.100.198/handle/2S6PX9GI/14972]
专题	岩土力学所知识全产出_期刊论文
作者单位	1.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Hubei, Peoples R China; 2.Nanjing Univ, Sch Earth Sci & Engn, Nanjing 210023, Jiangsu, Peoples R China; 3.China Univ Geosci Wuhan, Fac Engn, Wuhan 430074, Hubei, Peoples R China
推荐引用方式 GB/T 7714	Feng, Guangliang,Xia, Guoqing,Chen, Bingrui,et al. A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model[J]. SUSTAINABILITY,2019,11(11):-.
APA	Feng, Guangliang,Xia, Guoqing,Chen, Bingrui,Xiao, Yaxun,&Zhou, Ruichen.(2019).A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model.SUSTAINABILITY,11(11),-.
MLA	Feng, Guangliang,et al."A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model".SUSTAINABILITY 11.11(2019):-.