Open Access
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volume 11
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issue 1
,
publication number 24
Evaluation of initial geostress field of underground powerhouse in the complex alteration area of western Sichuan based on back-propagation neural network method
3
CCCC Highway Consultants CO., Ltd., Beijing, China
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Publication type: Journal Article
Publication date: 2025-03-05
scimago Q1
wos Q1
SJR: 1.027
CiteScore: 7.1
Impact factor: 4.2
ISSN: 23638419, 23638427
Abstract
Rock alteration leads to the degradation of the physical and mechanical properties of original rock. The spatial variance in the degree of rock alteration significantly influences regional ground stress distribution characteristics. The key problem in the process of ground stress inversion of 3-d geological model of underground powerhouse is that the geological model is difficult to divide reasonable alteration region, and the mechanical parameters of altered rock mass have strong non-uniformity. To solve these problems, a rapid and efficient classification criterion based on exploration-derived geological information was proposed. Additionally, this classification was used to analyze the distribution function of mechanical parameters in surrounding rocks across different alteration grades. The geological data obtained through exploration tunnels reveal that the spatial distribution characteristics of joint lineament density and altered rock development features are closely related to the fault. Based on the development mechanism of altered rocks, the distribution law of alteration is revealed from the perspective of engineering geology. And with the application of the back-propagation neural network method, a new three-dimensional ground stress inversion model that considers the distribution of altered rock was proposed. The inversion results indicate that the ground stress values obtained from the model, which considers the distribution of various alteration grades, correspond closely with the observed ground stress distribution characteristics. Moreover, this result proves that this inversion method is reasonable when obtaining the geostress field at engineering scale across the alteration region as evinced by a comparative study. This inversion method has good applicability to obtain the initial ground stress distribution in the area of structural alteration where the lithology difference is large but there is no obvious lithology boundary.
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Zheng H. et al. Evaluation of initial geostress field of underground powerhouse in the complex alteration area of western Sichuan based on back-propagation neural network method // Geomechanics and Geophysics for Geo-Energy and Geo-Resources. 2025. Vol. 11. No. 1. 24
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Zheng H., Cao S., Jiang Q., Li S., Xu D., Li Zhiwei Evaluation of initial geostress field of underground powerhouse in the complex alteration area of western Sichuan based on back-propagation neural network method // Geomechanics and Geophysics for Geo-Energy and Geo-Resources. 2025. Vol. 11. No. 1. 24
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TY - JOUR
DO - 10.1007/s40948-025-00949-z
UR - https://link.springer.com/10.1007/s40948-025-00949-z
TI - Evaluation of initial geostress field of underground powerhouse in the complex alteration area of western Sichuan based on back-propagation neural network method
T2 - Geomechanics and Geophysics for Geo-Energy and Geo-Resources
AU - Zheng, Hong
AU - Cao, Shiqi
AU - Jiang, Quan
AU - Li, Shaojun
AU - Xu, Dingping
AU - Li Zhiwei
PY - 2025
DA - 2025/03/05
PB - Springer Nature
IS - 1
VL - 11
SN - 2363-8419
SN - 2363-8427
ER -
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@article{2025_Zheng,
author = {Hong Zheng and Shiqi Cao and Quan Jiang and Shaojun Li and Dingping Xu and Li Zhiwei},
title = {Evaluation of initial geostress field of underground powerhouse in the complex alteration area of western Sichuan based on back-propagation neural network method},
journal = {Geomechanics and Geophysics for Geo-Energy and Geo-Resources},
year = {2025},
volume = {11},
publisher = {Springer Nature},
month = {mar},
url = {https://link.springer.com/10.1007/s40948-025-00949-z},
number = {1},
pages = {24},
doi = {10.1007/s40948-025-00949-z}
}