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Evaluation of input geological parameters and tunnel strain for strain-softening rock mass based on GSI

The regression analysis method is being widely adopted to analyse the tunnel strain, most of which ignore the strain-softening effect of the rock mass and fail to consider the influence of support pressure, initial stress state, and rock mass strength classification in one fitting equation. This stu...

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Autores principales: Cui, Lan, Sheng, Qian, Zhang, Jun, Dong, You-kou, Guo, Zhen-shan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708675/
https://www.ncbi.nlm.nih.gov/pubmed/36446816
http://dx.doi.org/10.1038/s41598-022-23587-x
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author Cui, Lan
Sheng, Qian
Zhang, Jun
Dong, You-kou
Guo, Zhen-shan
author_facet Cui, Lan
Sheng, Qian
Zhang, Jun
Dong, You-kou
Guo, Zhen-shan
author_sort Cui, Lan
collection PubMed
description The regression analysis method is being widely adopted to analyse the tunnel strain, most of which ignore the strain-softening effect of the rock mass and fail to consider the influence of support pressure, initial stress state, and rock mass strength classification in one fitting equation. This study aims to overcome these deficiencies with a regression model used to estimate the tunnel strain. A group of geological strength indexes (GSI) are configured to quantify the input strength parameters and deformation moduli for the rock mass with a quality ranging from poor to excellent. A specific semi-analytical procedure is developed to calculate the tunnel strain around a circular opening, which is validated by comparison with those using existing methods. A nonlinear regression model is then established to analyse the obtained tunnel strain, combining twelve fitting equations to relate the tunnel strain and the factors including the support pressure, GSI, initial stress state, and critical softening parameter. Particularly, three equations are for the estimation of the critical tunnel strain, critical support pressure, and tunnel strain under elastic behaviour, respectively; and the other nine equations are for the tunnel strain with different strain-softening behaviours. The relative significance between the GSI, the initial stress and the support pressure on the tunnel strain is assessed.
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spelling pubmed-97086752022-12-01 Evaluation of input geological parameters and tunnel strain for strain-softening rock mass based on GSI Cui, Lan Sheng, Qian Zhang, Jun Dong, You-kou Guo, Zhen-shan Sci Rep Article The regression analysis method is being widely adopted to analyse the tunnel strain, most of which ignore the strain-softening effect of the rock mass and fail to consider the influence of support pressure, initial stress state, and rock mass strength classification in one fitting equation. This study aims to overcome these deficiencies with a regression model used to estimate the tunnel strain. A group of geological strength indexes (GSI) are configured to quantify the input strength parameters and deformation moduli for the rock mass with a quality ranging from poor to excellent. A specific semi-analytical procedure is developed to calculate the tunnel strain around a circular opening, which is validated by comparison with those using existing methods. A nonlinear regression model is then established to analyse the obtained tunnel strain, combining twelve fitting equations to relate the tunnel strain and the factors including the support pressure, GSI, initial stress state, and critical softening parameter. Particularly, three equations are for the estimation of the critical tunnel strain, critical support pressure, and tunnel strain under elastic behaviour, respectively; and the other nine equations are for the tunnel strain with different strain-softening behaviours. The relative significance between the GSI, the initial stress and the support pressure on the tunnel strain is assessed. Nature Publishing Group UK 2022-11-29 /pmc/articles/PMC9708675/ /pubmed/36446816 http://dx.doi.org/10.1038/s41598-022-23587-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Cui, Lan
Sheng, Qian
Zhang, Jun
Dong, You-kou
Guo, Zhen-shan
Evaluation of input geological parameters and tunnel strain for strain-softening rock mass based on GSI
title Evaluation of input geological parameters and tunnel strain for strain-softening rock mass based on GSI
title_full Evaluation of input geological parameters and tunnel strain for strain-softening rock mass based on GSI
title_fullStr Evaluation of input geological parameters and tunnel strain for strain-softening rock mass based on GSI
title_full_unstemmed Evaluation of input geological parameters and tunnel strain for strain-softening rock mass based on GSI
title_short Evaluation of input geological parameters and tunnel strain for strain-softening rock mass based on GSI
title_sort evaluation of input geological parameters and tunnel strain for strain-softening rock mass based on gsi
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708675/
https://www.ncbi.nlm.nih.gov/pubmed/36446816
http://dx.doi.org/10.1038/s41598-022-23587-x
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