View in EDS

Rapid determination of on-site rock strength based on point load test and rebound test.

Saved in:
Bibliographic Details
Title: Rapid determination of on-site rock strength based on point load test and rebound test.
Authors: He, Quanjiang1,2 (AUTHOR), Wang, Yingchao1,2 (AUTHOR) wangyingchao@cumt.edu.cn, Zou, Hemin3 (AUTHOR), Zhang, Zheng4 (AUTHOR), Shao, Lichao3 (AUTHOR), Yang, Xiaochun3 (AUTHOR)
Source: Bulletin of Engineering Geology & the Environment. May2026, Vol. 85 Issue 5, p1-23. 23p.
Subjects: Rock testing, Rock properties, Metaheuristic algorithms, Tunnel design & construction, Random forest algorithms, Machine learning, Support vector machines
Abstract: The determination of rock strength parameters is a key issue in the design and construction of mountain tunnels. Traditional exploration methods or indoor experiments involve complex processes and provide limited data. Moreover, empirical formulas based on a single indicator fail to match the characteristics of different rock types, making it challenging to meet the requirements of rapid on-site construction. This study conducted on-site point load tests and rebound tests on typical sections of mountain tunnels with two different rock types, and proposed a rapid determination method for surrounding rock strength that integrates multiple on-site tests. Besides, using artificial intelligence technology, random forest models and least squares support vector machine models based on the bitterling fish optimization algorithm were established for sedimentary rocks and metamorphic rocks, respectively, thus achieving fast and efficient acquisition of rock strength parameters. Finally, data analysis of actual engineering projects proves that this method can effectively extract the variation patterns of rock parameters in different sections, and significantly reduce the time and cost of engineering decision-making, which has a significant impact on the safe construction of tunnel excavation. Highlights: Conducted on-site experiments to obtain data and verify the applicability of the method. Universal machine learning models based on different rock types were built. Established a model adapted for small samples used meta heuristic optimization algorithm. Combined laboratory experimental data with predicted data, compared and analyzied the results with real parameters. Reasonable conclusions were obtained and would provide reference for the application of engineering. [ABSTRACT FROM AUTHOR]
Copyright of Bulletin of Engineering Geology & the Environment is the property of Springer Nature 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
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 192769761
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Rapid determination of on-site rock strength based on point load test and rebound test.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22He%2C+Quanjiang%22">He, Quanjiang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yingchao%22">Wang, Yingchao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> wangyingchao@cumt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zou%2C+Hemin%22">Zou, Hemin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Zheng%22">Zhang, Zheng</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shao%2C+Lichao%22">Shao, Lichao</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Xiaochun%22">Yang, Xiaochun</searchLink><relatesTo>3</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Bulletin+of+Engineering+Geology+%26+the+Environment%22">Bulletin of Engineering Geology & the Environment</searchLink>. May2026, Vol. 85 Issue 5, p1-23. 23p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Rock+testing%22">Rock testing</searchLink><br /><searchLink fieldCode="DE" term="%22Rock+properties%22">Rock properties</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Tunnel+design+%26+construction%22">Tunnel design & construction</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The determination of rock strength parameters is a key issue in the design and construction of mountain tunnels. Traditional exploration methods or indoor experiments involve complex processes and provide limited data. Moreover, empirical formulas based on a single indicator fail to match the characteristics of different rock types, making it challenging to meet the requirements of rapid on-site construction. This study conducted on-site point load tests and rebound tests on typical sections of mountain tunnels with two different rock types, and proposed a rapid determination method for surrounding rock strength that integrates multiple on-site tests. Besides, using artificial intelligence technology, random forest models and least squares support vector machine models based on the bitterling fish optimization algorithm were established for sedimentary rocks and metamorphic rocks, respectively, thus achieving fast and efficient acquisition of rock strength parameters. Finally, data analysis of actual engineering projects proves that this method can effectively extract the variation patterns of rock parameters in different sections, and significantly reduce the time and cost of engineering decision-making, which has a significant impact on the safe construction of tunnel excavation. Highlights: Conducted on-site experiments to obtain data and verify the applicability of the method. Universal machine learning models based on different rock types were built. Established a model adapted for small samples used meta heuristic optimization algorithm. Combined laboratory experimental data with predicted data, compared and analyzied the results with real parameters. Reasonable conclusions were obtained and would provide reference for the application of engineering. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Bulletin of Engineering Geology & the Environment is the property of Springer Nature 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=192769761
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10064-026-04922-2
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 1
    Subjects:
      – SubjectFull: Rock testing
        Type: general
      – SubjectFull: Rock properties
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Tunnel design & construction
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Support vector machines
        Type: general
    Titles:
      – TitleFull: Rapid determination of on-site rock strength based on point load test and rebound test.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: He, Quanjiang
      – PersonEntity:
          Name:
            NameFull: Wang, Yingchao
      – PersonEntity:
          Name:
            NameFull: Zou, Hemin
      – PersonEntity:
          Name:
            NameFull: Zhang, Zheng
      – PersonEntity:
          Name:
            NameFull: Shao, Lichao
      – PersonEntity:
          Name:
            NameFull: Yang, Xiaochun
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 14359529
          Numbering:
            – Type: volume
              Value: 85
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Bulletin of Engineering Geology & the Environment
              Type: main
ResultId 1