Academic Journal
Estimation of compressive strength for spiral stirrup-confined circular concrete column using optimized machine learning with interpretable techniques.
| Τίτλος: | Estimation of compressive strength for spiral stirrup-confined circular concrete column using optimized machine learning with interpretable techniques. |
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| Συγγραφείς: | Sun, Yang |
| Πηγή: | Mechanics of Advanced Materials & Structures; 2024, Vol. 31 Issue 28, p10839-10858, 20p |
| Θεματικοί όροι: | Concrete columns, Machine learning, Concrete construction, Graphical user interfaces, Compressive strength, Boosting algorithms |
| Περίληψη: | The compressive strength (CS) of a concrete column confined with spiral stirrups is an important indicator for assessing the safety and stability of concrete structures. However, achieving accurate CS estimation for confined concrete remains challenging due to the complex confinement mechanism provided by spiral stirrups. In this study, three robust machine learning (ML) algorithms—support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost)—are employed to predict the CS value of the spiral stirrup-confined circular concrete columns. The hyperparameters of the ML models undergo fine-tuning via Bayesian optimization with 10-fold cross-validation, and the optimized ML models are evaluated for their predictive capabilities. Results show that compared to SVR and RF, XGBoost exhibits more stable generalization performance, achieving an average coefficient of determination (R2) of 0.944 for the 10-fold cross-validation, and demonstrates superior accuracy on the testing dataset with an R2 value of 0.967. To provide insights into the relationship between input features and the output CS value, Individual Conditional Exception (ICE) plots, one/two-dimensional Partial Dependence Plots (PDPs), and Shapley Additive Explanation (SHAP) techniques are utilized to interpret the optimized XGBoost model. Additionally, a friendly online graphical user interface (GUI) has been specially developed based on the optimized XGBoost model to facilitate convenient CS estimation for spiral stirrup-confined circular concrete column. [ABSTRACT FROM AUTHOR] |
| Copyright of Mechanics of Advanced Materials & Structures 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=15376494&ISBN=&volume=31&issue=28&date=20241229&spage=10839&pages=10839-10858&title=Mechanics of Advanced Materials & Structures&atitle=Estimation%20of%20compressive%20strength%20for%20spiral%20stirrup-confined%20circular%20concrete%20column%20using%20optimized%20machine%20learning%20with%20interpretable%20techniques.&aulast=Sun%2C%20Yang&id=DOI:10.1080/15376494.2023.2298232 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Estimation of compressive strength for spiral stirrup-confined circular concrete column using optimized machine learning with interpretable techniques. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sun%2C+Yang%22">Sun, Yang</searchLink> – Name: TitleSource Label: Source Group: Src Data: Mechanics of Advanced Materials & Structures; 2024, Vol. 31 Issue 28, p10839-10858, 20p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Concrete+columns%22">Concrete columns</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Concrete+construction%22">Concrete construction</searchLink><br /><searchLink fieldCode="DE" term="%22Graphical+user+interfaces%22">Graphical user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Compressive+strength%22">Compressive strength</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The compressive strength (CS) of a concrete column confined with spiral stirrups is an important indicator for assessing the safety and stability of concrete structures. However, achieving accurate CS estimation for confined concrete remains challenging due to the complex confinement mechanism provided by spiral stirrups. In this study, three robust machine learning (ML) algorithms—support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost)—are employed to predict the CS value of the spiral stirrup-confined circular concrete columns. The hyperparameters of the ML models undergo fine-tuning via Bayesian optimization with 10-fold cross-validation, and the optimized ML models are evaluated for their predictive capabilities. Results show that compared to SVR and RF, XGBoost exhibits more stable generalization performance, achieving an average coefficient of determination (R<superscript>2</superscript>) of 0.944 for the 10-fold cross-validation, and demonstrates superior accuracy on the testing dataset with an R<superscript>2</superscript> value of 0.967. To provide insights into the relationship between input features and the output CS value, Individual Conditional Exception (ICE) plots, one/two-dimensional Partial Dependence Plots (PDPs), and Shapley Additive Explanation (SHAP) techniques are utilized to interpret the optimized XGBoost model. Additionally, a friendly online graphical user interface (GUI) has been specially developed based on the optimized XGBoost model to facilitate convenient CS estimation for spiral stirrup-confined circular concrete column. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Mechanics of Advanced Materials & Structures 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/15376494.2023.2298232 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 10839 Subjects: – SubjectFull: Concrete columns Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Concrete construction Type: general – SubjectFull: Graphical user interfaces Type: general – SubjectFull: Compressive strength Type: general – SubjectFull: Boosting algorithms Type: general Titles: – TitleFull: Estimation of compressive strength for spiral stirrup-confined circular concrete column using optimized machine learning with interpretable techniques. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sun, Yang IsPartOfRelationships: – BibEntity: Dates: – D: 29 M: 12 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 15376494 Numbering: – Type: volume Value: 31 – Type: issue Value: 28 Titles: – TitleFull: Mechanics of Advanced Materials & Structures Type: main |
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