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Probabilistic quantification of geological strength index considering joint morphology and strength parameters.

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Title: Probabilistic quantification of geological strength index considering joint morphology and strength parameters.
Authors: Liu, Jian1 (AUTHOR), Jiang, Quan1 (AUTHOR) qjiang@whrsm.ac.cn, Song, Zebin1,2 (AUTHOR), He, Benguo3 (AUTHOR), Xu, Dingping1 (AUTHOR)
Source: Georisk: Assessment & Management of Risk for Engineered Systems & Geohazards. Mar2026, Vol. 20 Issue 1, p337-360. 24p.
Subjects: Bayesian analysis, Rock mechanics, Optical scanners, Rock properties
Abstract: Accurate assessment of rock mass strength is crucial in rock mass engineering due to its significant impact on project safety and economic viability. Improper evaluation of rock mass parameters can lead to potential risks or costly inefficiencies. In this study, an attempt was made to integrate the Hoek-Brown criterion and Barton-Bandis criterion to describe the strength of rock mass with non-persistent joints using a new analysis model framework. Further, a new quantitative evaluation method for the geological strength index (GSI) was proposed based on joint persistence factor, joint roughness coefficient, and residual friction angle. Rock core samples containing representative joints were selected from boreholes at the Yingliangbao Hydropower Station. The surfaces of these joints were then subjected to 3D laser scanning, and embedded into cubic cement molds for direct shear testing. Then, GSI values at different parts of the main powerhouse were obtained, with a mean of 62.3 and a standard deviation of 10.99. Furthermore, a Bayesian framework was employed to quantify the uncertainty of the proposed GSI model. By incorporating in-situ data, the analysis yields a posterior probability distribution for the GSI value, providing a more realistic assessment of rock mass quality. [ABSTRACT FROM AUTHOR]
Copyright of Georisk: Assessment & Management of Risk for Engineered Systems & Geohazards is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 191654326
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Probabilistic quantification of geological strength index considering joint morphology and strength parameters.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Jian%22">Liu, Jian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Quan%22">Jiang, Quan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> qjiang@whrsm.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Song%2C+Zebin%22">Song, Zebin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Benguo%22">He, Benguo</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Dingping%22">Xu, Dingping</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Rock+mechanics%22">Rock mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+scanners%22">Optical scanners</searchLink><br /><searchLink fieldCode="DE" term="%22Rock+properties%22">Rock properties</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Accurate assessment of rock mass strength is crucial in rock mass engineering due to its significant impact on project safety and economic viability. Improper evaluation of rock mass parameters can lead to potential risks or costly inefficiencies. In this study, an attempt was made to integrate the Hoek-Brown criterion and Barton-Bandis criterion to describe the strength of rock mass with non-persistent joints using a new analysis model framework. Further, a new quantitative evaluation method for the geological strength index (GSI) was proposed based on joint persistence factor, joint roughness coefficient, and residual friction angle. Rock core samples containing representative joints were selected from boreholes at the Yingliangbao Hydropower Station. The surfaces of these joints were then subjected to 3D laser scanning, and embedded into cubic cement molds for direct shear testing. Then, GSI values at different parts of the main powerhouse were obtained, with a mean of 62.3 and a standard deviation of 10.99. Furthermore, a Bayesian framework was employed to quantify the uncertainty of the proposed GSI model. By incorporating in-situ data, the analysis yields a posterior probability distribution for the GSI value, providing a more realistic assessment of rock mass quality. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Georisk: Assessment & Management of Risk for Engineered Systems & Geohazards is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=191654326
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/17499518.2025.2591758
    Languages:
      – Code: eng
        Text: English
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        PageCount: 24
        StartPage: 337
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      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Rock mechanics
        Type: general
      – SubjectFull: Optical scanners
        Type: general
      – SubjectFull: Rock properties
        Type: general
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      – TitleFull: Probabilistic quantification of geological strength index considering joint morphology and strength parameters.
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            NameFull: Liu, Jian
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            NameFull: Jiang, Quan
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            NameFull: Song, Zebin
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            NameFull: He, Benguo
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            NameFull: Xu, Dingping
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            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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