Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature

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@Article{ALASKAR:2023:cscm,

• author = "Abdulaziz Alaskar and Ghasan Alfalah and Fadi Althoey and Mohammed Awad Abuhussain and Muhammad Faisal Javed and Ahmed Farouk Deifalla and Nivin A. Ghamry",
• title = "Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature",
• journal = "Case Studies in Construction Materials",
• volume = "18",
• pages = "e02199",
• year = "2023",
• keywords = "genetic algorithms, genetic programming, gene expression programming",
• ISSN = "2214-5095",
• DOI = "doi:10.1016/j.cscm.2023.e02199",
• URL = "https://www.sciencedirect.com/science/article/pii/S2214509523003790",
• abstract = "The elevated temperature severely influences the mixed properties of concrete, causing a decrease in its strength properties. Accurate proportioning of concrete components for obtaining the required compressive strength (C-S) at elevated temperatures is a complicated and time-taking process. However, using evolutionary programming techniques such as gene expression programming (GEP) and multi-expression programming (MEP) provides the accurate prediction of concrete C-S. This article presents the genetic programming-based models (such as gene expression programming (GEP) and multi-expression programming (MEP)) for forecasting the concrete compressive strength (C-S) at elevated temperatures. In this regard, 207 C-S values at elevated temperatures were obtained from previous studies. In the model's development, C-S was considered as the output parameter with the nine most influential input parameters, including; Nano silica, cement, fly ash, water, temperature, silica fume, superplasticizer, sand, and gravels. The efficacy and accuracy of the GEP and MEP-based models were assessed by using statistical measures such as mean absolute error (MAE), correlation coefficient (R2), and root mean square error (RMSE). Moreover, models were also evaluated for external validation using different validation criteria recommended by previous studies. In comparing GEP and MEP models, GEP gave higher R2 and lower RMSE and MAE values of 0.854, 5.331 MPa, and 0.018 MPa respectively, indicating a strong correlation between actual and anticipated outputs. Thus, the GEP-based model was used further for sensitivity analysis, which revealed that cement is the most influencing factor. In addition, the proposed GEP model provides simple mathematical expression that can be easily implemented in practice",

}

Genetic Programming entries for Abdulaziz Alaskar Ghasan Alfalah Fadi Althoey Mohammed Awad Abuhussain Muhammad Faisal Javed Ahmed Farouk Mohamed Hassan Deifalla Nivin A Ghamry

Citations