Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete

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@Article{kovacevic:2025:Computation,

• author = "Miljan Kovacevic and Marijana Hadzima-Nyarko and Predrag Petronijevic and Tatijana Vasiljevic and Miroslav Radomirovic",
• title = "Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete",
• journal = "Computation",
• year = "2025",
• volume = "13",
• number = "1",
• pages = "Article No. 17",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2079-3197",
• URL = "https://www.mdpi.com/2079-3197/13/1/17",
• DOI = "doi:10.3390/computation13010017",
• abstract = "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.",
• notes = "also known as \cite{computation13010017}",

}

Genetic Programming entries for Miljan Kovacevic Marijana Hadzima-Nyarko Predrag Petronijevic Tatijana Vasiljevic Miroslav Radomirovic

Citations