Explainable Data-Driven Ensemble Learning Models for the Mechanical Properties Prediction of Concrete Confined by Aramid Fiber-Reinforced Polymer Wraps Using Generative Adversarial Networks
The current study offers a data-driven methodology to predict the ultimate strain and compressive strength of concrete reinforced by aramid FRP wraps. An experimental database was collected from the literature, on which seven different machine learning (ML) models were trained. The diameter and leng...
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MDPI AG
2023-11-01
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| author | Celal Cakiroglu |
| author_facet | Celal Cakiroglu |
| author_sort | Celal Cakiroglu |
| collection | DOAJ |
| description | The current study offers a data-driven methodology to predict the ultimate strain and compressive strength of concrete reinforced by aramid FRP wraps. An experimental database was collected from the literature, on which seven different machine learning (ML) models were trained. The diameter and length of the cylindrical specimens, the compressive strength of unconfined concrete, the thickness, elasticity modulus and ultimate tensile strength of the FRP wrap were used as the input features of the machine learning models, to predict the ultimate strength and strain of the specimens. The experimental dataset was further enhanced with synthetic data using the tabular generative adversarial network (TGAN) approach. The machine learning models’ performances were compared to the predictions of the existing strain capacity and compressive strength prediction equations for aramid FRP-confined concrete. The accuracy of the predictive models was measured using state-of-the-art statistical metrics such as the coefficient of determination, mean absolute error and root mean squared error. On average, the machine learning models were found to perform better than the available equations in the literature. In particular, the extra trees regressor, XGBoost and K-nearest neighbors algorithms performed significantly better than the remaining algorithms, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> scores greater than 0.98. Furthermore, the SHapley Additive exPlanations (SHAP) method and individual conditional expectation (ICE) plots were used to visualize the effects of various input parameters on the predicted ultimate strain and strength values. The unconfined compressive strength of concrete and the ultimate tensile strength of the FRP wrap were found to have the greatest impact on the machine learning model outputs. |
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| spelling | doaj.art-21c3d33ebea24055aa51c73428ed268a2023-11-10T14:59:24ZengMDPI AGApplied Sciences2076-34172023-11-0113211199110.3390/app132111991Explainable Data-Driven Ensemble Learning Models for the Mechanical Properties Prediction of Concrete Confined by Aramid Fiber-Reinforced Polymer Wraps Using Generative Adversarial NetworksCelal Cakiroglu0Department of Civil Engineering, Turkish-German University, Istanbul 34820, TurkeyThe current study offers a data-driven methodology to predict the ultimate strain and compressive strength of concrete reinforced by aramid FRP wraps. An experimental database was collected from the literature, on which seven different machine learning (ML) models were trained. The diameter and length of the cylindrical specimens, the compressive strength of unconfined concrete, the thickness, elasticity modulus and ultimate tensile strength of the FRP wrap were used as the input features of the machine learning models, to predict the ultimate strength and strain of the specimens. The experimental dataset was further enhanced with synthetic data using the tabular generative adversarial network (TGAN) approach. The machine learning models’ performances were compared to the predictions of the existing strain capacity and compressive strength prediction equations for aramid FRP-confined concrete. The accuracy of the predictive models was measured using state-of-the-art statistical metrics such as the coefficient of determination, mean absolute error and root mean squared error. On average, the machine learning models were found to perform better than the available equations in the literature. In particular, the extra trees regressor, XGBoost and K-nearest neighbors algorithms performed significantly better than the remaining algorithms, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> scores greater than 0.98. Furthermore, the SHapley Additive exPlanations (SHAP) method and individual conditional expectation (ICE) plots were used to visualize the effects of various input parameters on the predicted ultimate strain and strength values. The unconfined compressive strength of concrete and the ultimate tensile strength of the FRP wrap were found to have the greatest impact on the machine learning model outputs.https://www.mdpi.com/2076-3417/13/21/11991aramid fiber reinforced polymersmachine learningcompressive strengthconcrete confinementXGBoostSHAP |
| spellingShingle | Celal Cakiroglu Explainable Data-Driven Ensemble Learning Models for the Mechanical Properties Prediction of Concrete Confined by Aramid Fiber-Reinforced Polymer Wraps Using Generative Adversarial Networks Applied Sciences aramid fiber reinforced polymers machine learning compressive strength concrete confinement XGBoost SHAP |
| title | Explainable Data-Driven Ensemble Learning Models for the Mechanical Properties Prediction of Concrete Confined by Aramid Fiber-Reinforced Polymer Wraps Using Generative Adversarial Networks |
| title_full | Explainable Data-Driven Ensemble Learning Models for the Mechanical Properties Prediction of Concrete Confined by Aramid Fiber-Reinforced Polymer Wraps Using Generative Adversarial Networks |
| title_fullStr | Explainable Data-Driven Ensemble Learning Models for the Mechanical Properties Prediction of Concrete Confined by Aramid Fiber-Reinforced Polymer Wraps Using Generative Adversarial Networks |
| title_full_unstemmed | Explainable Data-Driven Ensemble Learning Models for the Mechanical Properties Prediction of Concrete Confined by Aramid Fiber-Reinforced Polymer Wraps Using Generative Adversarial Networks |
| title_short | Explainable Data-Driven Ensemble Learning Models for the Mechanical Properties Prediction of Concrete Confined by Aramid Fiber-Reinforced Polymer Wraps Using Generative Adversarial Networks |
| title_sort | explainable data driven ensemble learning models for the mechanical properties prediction of concrete confined by aramid fiber reinforced polymer wraps using generative adversarial networks |
| topic | aramid fiber reinforced polymers machine learning compressive strength concrete confinement XGBoost SHAP |
| url | https://www.mdpi.com/2076-3417/13/21/11991 |
| work_keys_str_mv | AT celalcakiroglu explainabledatadrivenensemblelearningmodelsforthemechanicalpropertiespredictionofconcreteconfinedbyaramidfiberreinforcedpolymerwrapsusinggenerativeadversarialnetworks |