Prediction of the axial compression capacity of stub CFST columns using machine learning techniques
Abstract Concrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing unce...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-53352-1 |
| _version_ | 1797274779049263104 |
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| author | Khaled Megahed Nabil Said Mahmoud Saad Elden Mostafa Abd-Rabou |
| author_facet | Khaled Megahed Nabil Said Mahmoud Saad Elden Mostafa Abd-Rabou |
| author_sort | Khaled Megahed |
| collection | DOAJ |
| description | Abstract Concrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing uncertainty for engineering designers. Moreover, conventional regression analysis fails to accurately predict the intricate relationship between column properties and compressive strength. To address these issues, this study proposes the use of two machine learning (ML) models—Gaussian process regression (GPR) and symbolic regression (SR). These models accept a variety of input variables, encompassing geometric and material properties of stub CFST columns, to estimate their strength. An experimental database of 1316 specimens was compiled from various research papers, including circular, rectangular, and double-skin stub CFST columns. In addition, a dimensionless output variable, referred to as the strength index, is introduced to enhance model performance. To validate the efficiency of the introduced models, predictions from these models are compared with those from two established standard codes and various ML algorithms, including support vector regression optimized with particle swarm optimization (PSVR), artificial neural networks, XGBoost (XGB), CatBoost (CATB), Random Forest, and LightGBM models. Through performance metrics, the CATB, GPR, PSVR and XGB models emerge as the most accurate and reliable models from the evaluation results. In addition, simple and practical design equations for the different types of CFST columns have been proposed based on the SR model. The developed ML models and proposed equations can predict the compressive strength of stub CFST columns with reliable and accurate results, making them valuable tools for structural engineering. Furthermore, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The results of the feature analysis reveal that section slenderness ratio and concrete strength parameters negatively impact the compressive strength index. |
| first_indexed | 2024-03-07T15:03:44Z |
| format | Article |
| id | doaj.art-d75020ecce354f78a5d738df3bba7bb8 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:03:44Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-d75020ecce354f78a5d738df3bba7bb82024-03-05T19:01:00ZengNature PortfolioScientific Reports2045-23222024-02-0114111410.1038/s41598-024-53352-1Prediction of the axial compression capacity of stub CFST columns using machine learning techniquesKhaled Megahed0Nabil Said Mahmoud1Saad Elden Mostafa Abd-Rabou2Department of Structural Engineering, Mansoura UniversityDepartment of Structural Engineering, Mansoura UniversityDepartment of Structural Engineering, Mansoura UniversityAbstract Concrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing uncertainty for engineering designers. Moreover, conventional regression analysis fails to accurately predict the intricate relationship between column properties and compressive strength. To address these issues, this study proposes the use of two machine learning (ML) models—Gaussian process regression (GPR) and symbolic regression (SR). These models accept a variety of input variables, encompassing geometric and material properties of stub CFST columns, to estimate their strength. An experimental database of 1316 specimens was compiled from various research papers, including circular, rectangular, and double-skin stub CFST columns. In addition, a dimensionless output variable, referred to as the strength index, is introduced to enhance model performance. To validate the efficiency of the introduced models, predictions from these models are compared with those from two established standard codes and various ML algorithms, including support vector regression optimized with particle swarm optimization (PSVR), artificial neural networks, XGBoost (XGB), CatBoost (CATB), Random Forest, and LightGBM models. Through performance metrics, the CATB, GPR, PSVR and XGB models emerge as the most accurate and reliable models from the evaluation results. In addition, simple and practical design equations for the different types of CFST columns have been proposed based on the SR model. The developed ML models and proposed equations can predict the compressive strength of stub CFST columns with reliable and accurate results, making them valuable tools for structural engineering. Furthermore, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The results of the feature analysis reveal that section slenderness ratio and concrete strength parameters negatively impact the compressive strength index.https://doi.org/10.1038/s41598-024-53352-1 |
| spellingShingle | Khaled Megahed Nabil Said Mahmoud Saad Elden Mostafa Abd-Rabou Prediction of the axial compression capacity of stub CFST columns using machine learning techniques Scientific Reports |
| title | Prediction of the axial compression capacity of stub CFST columns using machine learning techniques |
| title_full | Prediction of the axial compression capacity of stub CFST columns using machine learning techniques |
| title_fullStr | Prediction of the axial compression capacity of stub CFST columns using machine learning techniques |
| title_full_unstemmed | Prediction of the axial compression capacity of stub CFST columns using machine learning techniques |
| title_short | Prediction of the axial compression capacity of stub CFST columns using machine learning techniques |
| title_sort | prediction of the axial compression capacity of stub cfst columns using machine learning techniques |
| url | https://doi.org/10.1038/s41598-024-53352-1 |
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