View in EDS HTML Full Text PDF Full Text

Machine Learning Prediction of the Compressive Bearing Capacity of Concrete-Filled Steel Tubes Using Random Forest.

Saved in:
Bibliographic Details
Title: Machine Learning Prediction of the Compressive Bearing Capacity of Concrete-Filled Steel Tubes Using Random Forest.
Authors: Su, Weidi1,2 (AUTHOR), Cheng, Yaofei1,2,3 (AUTHOR), Wei, Li2,3 (AUTHOR) 2210391066@st.gxu.edu.cn, Zhong, Guangda1,2,4 (AUTHOR), Zhou, Linxiao1,3 (AUTHOR), Liu, Fei2,3,4 (AUTHOR), Xie, Kaizhong2,4 (AUTHOR)
Source: Materials (1996-1944). Jun2026, Vol. 19 Issue 12, p2511. 27p.
Subjects: Random forest algorithms, Compressive strength, Machine learning, Structural engineering, Concrete-filled tubes, Prediction models, Mechanical behavior of materials
Abstract: Highlights: High-precision RF model for CFST capacity using 154 tests and 24 inputs. Steel tube inertia (Is) dominates prediction (35.56% contribution). Validated by nine CFST tests with prediction errors within 5%. Concrete-filled steel tube (CFST) members are widely used in long-span and high-rise structures due to their high load-bearing capacity and structural efficiency. Accurate prediction of their compressive bearing capacity is essential for reliable design. In this study, a data-driven prediction model based on the Random Forest (RF) algorithm was developed using a database of 154 axial compression tests. A total of 24 parameters, including geometric dimensions, material properties, and sectional characteristics, were considered as input variables, and the model was optimized through five-fold cross-validation and hyperparameter tuning. The results indicate that the proposed model achieves high accuracy and stability, with mean predicted-to-experimental ratios of 1.002 and 0.989 for the training and testing sets, respectively, and maximum deviations within 15%. Compared with existing design codes and alternative machine learning methods, the RF model improves prediction accuracy by approximately 9% and exhibits strong generalization capability. Furthermore, independent experimental validation using nine CFST column tests confirms its reliability, with prediction errors within 5%. These findings demonstrate that the proposed model provides an effective and practical tool for predicting the compressive bearing capacity of CFST members in engineering applications. [ABSTRACT FROM AUTHOR]
Copyright of Materials (1996-1944) is the property of MDPI 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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 194907585
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Machine Learning Prediction of the Compressive Bearing Capacity of Concrete-Filled Steel Tubes Using Random Forest.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Su%2C+Weidi%22">Su, Weidi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cheng%2C+Yaofei%22">Cheng, Yaofei</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Li%22">Wei, Li</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> 2210391066@st.gxu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhong%2C+Guangda%22">Zhong, Guangda</searchLink><relatesTo>1,2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Linxiao%22">Zhou, Linxiao</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Fei%22">Liu, Fei</searchLink><relatesTo>2,3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xie%2C+Kaizhong%22">Xie, Kaizhong</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Materials+%281996-1944%29%22">Materials (1996-1944)</searchLink>. Jun2026, Vol. 19 Issue 12, p2511. 27p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Compressive+strength%22">Compressive strength</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+engineering%22">Structural engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Concrete-filled+tubes%22">Concrete-filled tubes</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+behavior+of+materials%22">Mechanical behavior of materials</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: High-precision RF model for CFST capacity using 154 tests and 24 inputs. Steel tube inertia (Is) dominates prediction (35.56% contribution). Validated by nine CFST tests with prediction errors within 5%. Concrete-filled steel tube (CFST) members are widely used in long-span and high-rise structures due to their high load-bearing capacity and structural efficiency. Accurate prediction of their compressive bearing capacity is essential for reliable design. In this study, a data-driven prediction model based on the Random Forest (RF) algorithm was developed using a database of 154 axial compression tests. A total of 24 parameters, including geometric dimensions, material properties, and sectional characteristics, were considered as input variables, and the model was optimized through five-fold cross-validation and hyperparameter tuning. The results indicate that the proposed model achieves high accuracy and stability, with mean predicted-to-experimental ratios of 1.002 and 0.989 for the training and testing sets, respectively, and maximum deviations within 15%. Compared with existing design codes and alternative machine learning methods, the RF model improves prediction accuracy by approximately 9% and exhibits strong generalization capability. Furthermore, independent experimental validation using nine CFST column tests confirms its reliability, with prediction errors within 5%. These findings demonstrate that the proposed model provides an effective and practical tool for predicting the compressive bearing capacity of CFST members in engineering applications. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Materials (1996-1944) is the property of MDPI 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=194907585
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/ma19122511
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 2511
    Subjects:
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Compressive strength
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Structural engineering
        Type: general
      – SubjectFull: Concrete-filled tubes
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Mechanical behavior of materials
        Type: general
    Titles:
      – TitleFull: Machine Learning Prediction of the Compressive Bearing Capacity of Concrete-Filled Steel Tubes Using Random Forest.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Su, Weidi
      – PersonEntity:
          Name:
            NameFull: Cheng, Yaofei
      – PersonEntity:
          Name:
            NameFull: Wei, Li
      – PersonEntity:
          Name:
            NameFull: Zhong, Guangda
      – PersonEntity:
          Name:
            NameFull: Zhou, Linxiao
      – PersonEntity:
          Name:
            NameFull: Liu, Fei
      – PersonEntity:
          Name:
            NameFull: Xie, Kaizhong
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961944
          Numbering:
            – Type: volume
              Value: 19
            – Type: issue
              Value: 12
          Titles:
            – TitleFull: Materials (1996-1944)
              Type: main
ResultId 1