Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case Study

In mountain and ravine region of southwest China, several hydropower stations have been built in this area. It is the main national base for hydropower energy development, but under the influence of geological structure and surface erosion, the in-situ stress environment in this area is more complex...

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Main Authors: Gan Li, Yu Hu, Qing-Bin Li, Tao Yin, Jian-Xin Miao, Mengdi Yao
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9026983/
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spelling doaj-6e58e4ea104b43c3ba305b83355e0e0e2021-03-30T02:49:06ZengIEEEIEEE Access2169-35362020-01-018467014671210.1109/ACCESS.2020.29790249026983Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case StudyGan Li0https://orcid.org/0000-0003-4066-9529Yu Hu1Qing-Bin Li2Tao Yin3Jian-Xin Miao4Mengdi Yao5State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, ChinaState Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, ChinaState Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, ChinaState Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, ChinaChina Three Gorges Corporation, Beijing, ChinaChina Three Gorges Corporation, Beijing, ChinaIn mountain and ravine region of southwest China, several hydropower stations have been built in this area. It is the main national base for hydropower energy development, but under the influence of geological structure and surface erosion, the in-situ stress environment in this area is more complex. The main purpose of this paper is to establish the relationship between measured in-situ stress data and numerical calculation by means of neural network. Therefore, it can be used to analyze the distribution characteristics of large-scale in-situ stress and the failure of regional geological bodies under the action of in-situ stress in Xiluodu area. This paper analyzes the geological conditions and the measured data of in-situ stress in Xiluodu area. The results show that there is obvious surface weathering phenomenon in this area, the stress environment is complex. The depth has a positive impact on the level of in-situ stress, but the impact degree is different. The genetic algorithm-BP artificial neural networks (G-P) method is trained by the measured in-situ stress data. Based on the field measurement data, neural network algorithm and numerical simulation technology, the three-dimensional in-situ stress field distribution characteristics of the project area are carried out. The research shows that the scheme of combining the actual measurement, numerical analysis and neural network inversion is reliable; Depth is an important factor affecting the maximum horizontal stress value in Xiluodu area; Under the action of in-situ stress, the fissures of geological body are relatively developed, and three types of original water storage and flow spaces are formed before dam storage period; The flow and storage space of groundwater includes limestone fracture, basalt fracture and slope fracture aquifer; After the impoundment of the reservoir area, the reservoir water is transmitted downward through the vertical fissures, and finally the reservoir water forms a hydraulic connection with the groundwater. After impoundment, the hydrogeological conditions of the reservoir area would be changed.https://ieeexplore.ieee.org/document/9026983/In-situ stressnumerical simulationneural networkin-situ stress inversiongeological rock mass damagehydraulic connection
collection DOAJ
language English
format Article
sources DOAJ
author Gan Li
Yu Hu
Qing-Bin Li
Tao Yin
Jian-Xin Miao
Mengdi Yao
spellingShingle Gan Li
Yu Hu
Qing-Bin Li
Tao Yin
Jian-Xin Miao
Mengdi Yao
Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case Study
IEEE Access
In-situ stress
numerical simulation
neural network
in-situ stress inversion
geological rock mass damage
hydraulic connection
author_facet Gan Li
Yu Hu
Qing-Bin Li
Tao Yin
Jian-Xin Miao
Mengdi Yao
author_sort Gan Li
title Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case Study
title_short Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case Study
title_full Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case Study
title_fullStr Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case Study
title_full_unstemmed Inversion Method of In-situ Stress and Rock Damage Characteristics in Dam Site Using Neural Network and Numerical Simulation—A Case Study
title_sort inversion method of in-situ stress and rock damage characteristics in dam site using neural network and numerical simulation—a case study
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description In mountain and ravine region of southwest China, several hydropower stations have been built in this area. It is the main national base for hydropower energy development, but under the influence of geological structure and surface erosion, the in-situ stress environment in this area is more complex. The main purpose of this paper is to establish the relationship between measured in-situ stress data and numerical calculation by means of neural network. Therefore, it can be used to analyze the distribution characteristics of large-scale in-situ stress and the failure of regional geological bodies under the action of in-situ stress in Xiluodu area. This paper analyzes the geological conditions and the measured data of in-situ stress in Xiluodu area. The results show that there is obvious surface weathering phenomenon in this area, the stress environment is complex. The depth has a positive impact on the level of in-situ stress, but the impact degree is different. The genetic algorithm-BP artificial neural networks (G-P) method is trained by the measured in-situ stress data. Based on the field measurement data, neural network algorithm and numerical simulation technology, the three-dimensional in-situ stress field distribution characteristics of the project area are carried out. The research shows that the scheme of combining the actual measurement, numerical analysis and neural network inversion is reliable; Depth is an important factor affecting the maximum horizontal stress value in Xiluodu area; Under the action of in-situ stress, the fissures of geological body are relatively developed, and three types of original water storage and flow spaces are formed before dam storage period; The flow and storage space of groundwater includes limestone fracture, basalt fracture and slope fracture aquifer; After the impoundment of the reservoir area, the reservoir water is transmitted downward through the vertical fissures, and finally the reservoir water forms a hydraulic connection with the groundwater. After impoundment, the hydrogeological conditions of the reservoir area would be changed.
topic In-situ stress
numerical simulation
neural network
in-situ stress inversion
geological rock mass damage
hydraulic connection
url https://ieeexplore.ieee.org/document/9026983/
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