Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete
This study presents a detailed analysis of various machine learning models for predicting the interfacial bond strength of fiber-reinforced polymer (FRP) concrete, including multiple linear regression, Multigene Genetic Programming (MGGP), an ensemble of regression trees, Gaussian Process Regression...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/13/1/17 |
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| author | Miljan Kovačević Marijana Hadzima-Nyarko Predrag Petronijević Tatijana Vasiljević Miroslav Radomirović |
| author_facet | Miljan Kovačević Marijana Hadzima-Nyarko Predrag Petronijević Tatijana Vasiljević Miroslav Radomirović |
| author_sort | Miljan Kovačević |
| collection | DOAJ |
| description | This study presents a detailed analysis of various machine learning models for predicting the interfacial bond strength of fiber-reinforced polymer (FRP) concrete, including multiple linear regression, Multigene Genetic Programming (MGGP), an ensemble of regression trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), and neural networks. The evaluation was based on their predictive accuracy. The optimal model identified was the GPR ARD Exponential model, which achieved a mean absolute error (MAE) of 1.8953 MPa and a correlation coefficient (R) of 0.9658. An analysis of this optimal model highlighted the most influential variables affecting the bond strength. Additionally, the research identified several models with lower expression complexity and reduced accuracy, which may still be applicable in practical scenarios. |
| format | Article |
| id | doaj-art-04a38da0e9f44b43af765a8aa1610228 |
| institution | Kabale University |
| issn | 2079-3197 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj-art-04a38da0e9f44b43af765a8aa16102282025-01-24T13:27:49ZengMDPI AGComputation2079-31972025-01-011311710.3390/computation13010017Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-ConcreteMiljan Kovačević0Marijana Hadzima-Nyarko1Predrag Petronijević2Tatijana Vasiljević3Miroslav Radomirović4Faculty of Technical Sciences, University of Pristina, Knjaza Milosa 7, 38220 Kosovska Mitrovica, SerbiaFaculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 3, 31000 Osijek, CroatiaFaculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, SerbiaFaculty of Technical Sciences, University of Pristina, Knjaza Milosa 7, 38220 Kosovska Mitrovica, SerbiaFaculty of Technical Sciences, University of Pristina, Knjaza Milosa 7, 38220 Kosovska Mitrovica, SerbiaThis study presents a detailed analysis of various machine learning models for predicting the interfacial bond strength of fiber-reinforced polymer (FRP) concrete, including multiple linear regression, Multigene Genetic Programming (MGGP), an ensemble of regression trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), and neural networks. The evaluation was based on their predictive accuracy. The optimal model identified was the GPR ARD Exponential model, which achieved a mean absolute error (MAE) of 1.8953 MPa and a correlation coefficient (R) of 0.9658. An analysis of this optimal model highlighted the most influential variables affecting the bond strength. Additionally, the research identified several models with lower expression complexity and reduced accuracy, which may still be applicable in practical scenarios.https://www.mdpi.com/2079-3197/13/1/17fiber-reinforced polymerbond strengthmachine learning models |
| spellingShingle | Miljan Kovačević Marijana Hadzima-Nyarko Predrag Petronijević Tatijana Vasiljević Miroslav Radomirović Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete Computation fiber-reinforced polymer bond strength machine learning models |
| title | Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete |
| title_full | Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete |
| title_fullStr | Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete |
| title_full_unstemmed | Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete |
| title_short | Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete |
| title_sort | comparative analysis of machine learning models for predicting interfacial bond strength of fiber reinforced polymer concrete |
| topic | fiber-reinforced polymer bond strength machine learning models |
| url | https://www.mdpi.com/2079-3197/13/1/17 |
| work_keys_str_mv | AT miljankovacevic comparativeanalysisofmachinelearningmodelsforpredictinginterfacialbondstrengthoffiberreinforcedpolymerconcrete AT marijanahadzimanyarko comparativeanalysisofmachinelearningmodelsforpredictinginterfacialbondstrengthoffiberreinforcedpolymerconcrete AT predragpetronijevic comparativeanalysisofmachinelearningmodelsforpredictinginterfacialbondstrengthoffiberreinforcedpolymerconcrete AT tatijanavasiljevic comparativeanalysisofmachinelearningmodelsforpredictinginterfacialbondstrengthoffiberreinforcedpolymerconcrete AT miroslavradomirovic comparativeanalysisofmachinelearningmodelsforpredictinginterfacialbondstrengthoffiberreinforcedpolymerconcrete |