Hoek-Brown Failure Criterion-Based Creep Constitutive Model and BP Neural Network Parameter Inversion for Soft Surrounding Rock Mass of Tunnels

This paper summarizes the main factors affecting the large deformation of soft rock tunnels, including the lithology combination, weathering effect, and underground water status, by reviewing the typical cases of largely-deformed soft rock tunnels. The engineering geological properties of the rock m...

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Autores principales:	Chao Chen, Tianbin Li, Chunchi Ma, Hang Zhang, Jieling Tang, Yin Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	tunnel engineering soft rock creep parameter parameter inversion BP neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/c5e2d9d5c8af420997e881ba9912a2c2
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spelling	oai:doaj.org-article:c5e2d9d5c8af420997e881ba9912a2c22021-11-11T15:06:57ZHoek-Brown Failure Criterion-Based Creep Constitutive Model and BP Neural Network Parameter Inversion for Soft Surrounding Rock Mass of Tunnels10.3390/app1121100332076-3417https://doaj.org/article/c5e2d9d5c8af420997e881ba9912a2c22021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10033https://doaj.org/toc/2076-3417This paper summarizes the main factors affecting the large deformation of soft rock tunnels, including the lithology combination, weathering effect, and underground water status, by reviewing the typical cases of largely-deformed soft rock tunnels. The engineering geological properties of the rock mass were quantified using the rock mass block index (<i>RBI</i>) and the absolute weathering index (<i>AWI</i>) to calculate the geological strength index (<i>GSI</i>). Then, the long-term strength <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>σ</mi><mi>r</mi></msub></mrow></semantics></math></inline-formula> and the elastic modulus <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>E</mi><mn>0</mn></msub></mrow></semantics></math></inline-formula> of the rock mass were calculated according to the Hoek–Brown failure criterion and substituted into the creep constitutive model based on the Nashihara model. Finally, the creep parameters of the surrounding rock mass of the Ganbao tunnel were inverted and validated by integrating the on-site monitoring and BP neural network. The inversion results were consistent with the measured convergence during monitoring and satisfied the engineering requirements of accuracy. The method proposed in this paper can be used to invert the geological parameters of the surrounding rock mass for a certain point, which can provide important mechanical parameters for the design and construction of tunnels, and ensure the stability of the surrounding rock mass during the period of construction and the safety of the lining structure during operation.Chao ChenTianbin LiChunchi MaHang ZhangJieling TangYin ZhangMDPI AGarticletunnel engineeringsoft rockcreep parameterparameter inversionBP neural networkTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10033, p 10033 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	tunnel engineering soft rock creep parameter parameter inversion BP neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	tunnel engineering soft rock creep parameter parameter inversion BP neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Chao Chen Tianbin Li Chunchi Ma Hang Zhang Jieling Tang Yin Zhang Hoek-Brown Failure Criterion-Based Creep Constitutive Model and BP Neural Network Parameter Inversion for Soft Surrounding Rock Mass of Tunnels
description	This paper summarizes the main factors affecting the large deformation of soft rock tunnels, including the lithology combination, weathering effect, and underground water status, by reviewing the typical cases of largely-deformed soft rock tunnels. The engineering geological properties of the rock mass were quantified using the rock mass block index (<i>RBI</i>) and the absolute weathering index (<i>AWI</i>) to calculate the geological strength index (<i>GSI</i>). Then, the long-term strength <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>σ</mi><mi>r</mi></msub></mrow></semantics></math></inline-formula> and the elastic modulus <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>E</mi><mn>0</mn></msub></mrow></semantics></math></inline-formula> of the rock mass were calculated according to the Hoek–Brown failure criterion and substituted into the creep constitutive model based on the Nashihara model. Finally, the creep parameters of the surrounding rock mass of the Ganbao tunnel were inverted and validated by integrating the on-site monitoring and BP neural network. The inversion results were consistent with the measured convergence during monitoring and satisfied the engineering requirements of accuracy. The method proposed in this paper can be used to invert the geological parameters of the surrounding rock mass for a certain point, which can provide important mechanical parameters for the design and construction of tunnels, and ensure the stability of the surrounding rock mass during the period of construction and the safety of the lining structure during operation.
format	article
author	Chao Chen Tianbin Li Chunchi Ma Hang Zhang Jieling Tang Yin Zhang
author_facet	Chao Chen Tianbin Li Chunchi Ma Hang Zhang Jieling Tang Yin Zhang
author_sort	Chao Chen
title	Hoek-Brown Failure Criterion-Based Creep Constitutive Model and BP Neural Network Parameter Inversion for Soft Surrounding Rock Mass of Tunnels
title_short	Hoek-Brown Failure Criterion-Based Creep Constitutive Model and BP Neural Network Parameter Inversion for Soft Surrounding Rock Mass of Tunnels
title_full	Hoek-Brown Failure Criterion-Based Creep Constitutive Model and BP Neural Network Parameter Inversion for Soft Surrounding Rock Mass of Tunnels
title_fullStr	Hoek-Brown Failure Criterion-Based Creep Constitutive Model and BP Neural Network Parameter Inversion for Soft Surrounding Rock Mass of Tunnels
title_full_unstemmed	Hoek-Brown Failure Criterion-Based Creep Constitutive Model and BP Neural Network Parameter Inversion for Soft Surrounding Rock Mass of Tunnels
title_sort	hoek-brown failure criterion-based creep constitutive model and bp neural network parameter inversion for soft surrounding rock mass of tunnels
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c5e2d9d5c8af420997e881ba9912a2c2
work_keys_str_mv	AT chaochen hoekbrownfailurecriterionbasedcreepconstitutivemodelandbpneuralnetworkparameterinversionforsoftsurroundingrockmassoftunnels AT tianbinli hoekbrownfailurecriterionbasedcreepconstitutivemodelandbpneuralnetworkparameterinversionforsoftsurroundingrockmassoftunnels AT chunchima hoekbrownfailurecriterionbasedcreepconstitutivemodelandbpneuralnetworkparameterinversionforsoftsurroundingrockmassoftunnels AT hangzhang hoekbrownfailurecriterionbasedcreepconstitutivemodelandbpneuralnetworkparameterinversionforsoftsurroundingrockmassoftunnels AT jielingtang hoekbrownfailurecriterionbasedcreepconstitutivemodelandbpneuralnetworkparameterinversionforsoftsurroundingrockmassoftunnels AT yinzhang hoekbrownfailurecriterionbasedcreepconstitutivemodelandbpneuralnetworkparameterinversionforsoftsurroundingrockmassoftunnels
_version_	1718437174548889600

Hoek-Brown Failure Criterion-Based Creep Constitutive Model and BP Neural Network Parameter Inversion for Soft Surrounding Rock Mass of Tunnels

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