Symbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columns
Abstract Concrete-filled steel tube (CFST) columns are widely employed in high-rise buildings, long-span bridges, and seismic-resistant structures due to their superior load-bearing capacity, structural efficiency, and resilience under extreme loading conditions. This study uses symbolic regression...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85371-x |
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| author | Khaled Megahed |
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| author_sort | Khaled Megahed |
| collection | DOAJ |
| description | Abstract Concrete-filled steel tube (CFST) columns are widely employed in high-rise buildings, long-span bridges, and seismic-resistant structures due to their superior load-bearing capacity, structural efficiency, and resilience under extreme loading conditions. This study uses symbolic regression with structural design code provisions to predict the eccentric strength of concrete filled-steel tubular columns with circular shape (CCFST) and rectangular shape (RCFST). Previous studies have used two distinct approaches for estimating eccentric strength: explainable models based on theoretical derivations and black-box models derived from machine learning (ML) methods. This study proposes a hybrid model derived from the design code standards, with performance enhanced by the symbolic regression technique. This model is based on a comprehensive experimental database of 464 tests for CCFST columns and 313 tests for RCFST columns under eccentric loading from various research papers. The developed code-based symbolic regression (C-SR) displays both robust and interpretable, demonstrating high prediction accuracy with mean values of the prediction-to-actual ratios of 1.006 and 0.997 and coefficient of variation (CoV) values of 0.117 and 0.098 for CCFSTs and RCFSTs, respectively, while providing explainable mathematical expressions that align with the mechanical principles of code provisions. The developed C-SR model is benchmarked against EC4 and AISC360 standards and evaluated against the various ML techniques, demonstrating acceptable performance. The results highlight the C-SR model’s effectiveness in providing reliable predictions and valuable insights for practical engineering applications. |
| format | Article |
| id | doaj-art-34722ec69eb948158762e272909711c8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-34722ec69eb948158762e272909711c82025-01-26T12:27:35ZengNature PortfolioScientific Reports2045-23222025-01-0115111910.1038/s41598-025-85371-xSymbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columnsKhaled Megahed0Department of Structural Engineering, Mansoura UniversityAbstract Concrete-filled steel tube (CFST) columns are widely employed in high-rise buildings, long-span bridges, and seismic-resistant structures due to their superior load-bearing capacity, structural efficiency, and resilience under extreme loading conditions. This study uses symbolic regression with structural design code provisions to predict the eccentric strength of concrete filled-steel tubular columns with circular shape (CCFST) and rectangular shape (RCFST). Previous studies have used two distinct approaches for estimating eccentric strength: explainable models based on theoretical derivations and black-box models derived from machine learning (ML) methods. This study proposes a hybrid model derived from the design code standards, with performance enhanced by the symbolic regression technique. This model is based on a comprehensive experimental database of 464 tests for CCFST columns and 313 tests for RCFST columns under eccentric loading from various research papers. The developed code-based symbolic regression (C-SR) displays both robust and interpretable, demonstrating high prediction accuracy with mean values of the prediction-to-actual ratios of 1.006 and 0.997 and coefficient of variation (CoV) values of 0.117 and 0.098 for CCFSTs and RCFSTs, respectively, while providing explainable mathematical expressions that align with the mechanical principles of code provisions. The developed C-SR model is benchmarked against EC4 and AISC360 standards and evaluated against the various ML techniques, demonstrating acceptable performance. The results highlight the C-SR model’s effectiveness in providing reliable predictions and valuable insights for practical engineering applications.https://doi.org/10.1038/s41598-025-85371-xConcrete-filled-steel tubular columnsDesign code standardsMachine learningSymbolic regressionCCFSTRCFST |
| spellingShingle | Khaled Megahed Symbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columns Scientific Reports Concrete-filled-steel tubular columns Design code standards Machine learning Symbolic regression CCFST RCFST |
| title | Symbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columns |
| title_full | Symbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columns |
| title_fullStr | Symbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columns |
| title_full_unstemmed | Symbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columns |
| title_short | Symbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columns |
| title_sort | symbolic regression for strength prediction of eccentrically loaded concrete filled steel tubular columns |
| topic | Concrete-filled-steel tubular columns Design code standards Machine learning Symbolic regression CCFST RCFST |
| url | https://doi.org/10.1038/s41598-025-85371-x |
| work_keys_str_mv | AT khaledmegahed symbolicregressionforstrengthpredictionofeccentricallyloadedconcretefilledsteeltubularcolumns |