Symbolic regression model for predicting compression strength of steel fiber-reinforced concrete

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@Article{Le:2025:trpro,

• author = "Ba Anh Le and Hoang Quan Nguyen and Bao Viet Tran and Thai Son Vu and Thi Loan Bui",
• title = "Symbolic regression model for predicting compression strength of steel fiber-reinforced concrete",
• journal = "Transportation Research Procedia",
• year = "2025",
• volume = "85",
• pages = "94--101",
• note = "TRPRO SDCAT 2023",
• keywords = "genetic algorithms, genetic programming, Fiber Reinforced Concrete, Symbolic Regression model",
• ISSN = "2352-1465",
• URL = "https://www.sciencedirect.com/science/article/pii/S2352146525001978",
• DOI = "10.1016/j.trpro.2025.03.138",
• abstract = "black box' category, failing to provide a transparent formula. This article sets out to achieve the goal of developing a mathematical expression based on genetic programming to predict the compressive strength of fiber-reinforced concrete. A dataset comprising 166 entries, extracted from published journal papers and conference proceedings, has been used to train the model. The results obtained reveal that the formulated equation achieves a relatively high level of accuracy, with an R-squared value of 0.91. This outcome is further benchmarked against alternative machine learning models and demonstrates comparable performance",

}

Genetic Programming entries for Ba-Anh Le Hoang Quan Nguyen Bao-Viet Tran Thai-Son Vu Thi Loan Bui

Citations