Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{amin:2022:Materials,

• author = "Muhammad Nasir Amin and Muhammad Raheel and Mudassir Iqbal and Kaffayatullah Khan and Muhammad Ghulam Qadir and Fazal E. Jalal and Anas Abdulalim Alabdullah and Ali Ajwad and Majdi Adel Al-Faiad and Abdullah Mohammad Abu-Arab",
• title = "Prediction of Rapid Chloride Penetration Resistance to Assess the Influence of Affecting Variables on Metakaolin-Based Concrete Using Gene Expression Programming",
• journal = "Materials",
• year = "2022",
• volume = "15",
• number = "19",
• pages = "Article No. 6959",
• keywords = "genetic algorithms, genetic programming, gene expression programming",
• ISSN = "1996-1944",
• URL = "https://www.mdpi.com/1996-1944/15/19/6959",
• DOI = "doi:10.3390/ma15196959",
• abstract = "The useful life of a concrete structure is highly dependent upon its durability, which enables it to withstand the harsh environmental conditions. Resistance of a concrete specimen to rapid chloride ion penetration (RCP) is one of the tests to indirectly measure its durability. The central aim of this study was to investigate the influence of different variables, such as, age, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength of concrete on the RCP resistance using a genetic programming approach. The number of chromosomes (Nc), genes (Ng) and, the head size (Hs) of the gene expression programming (GEP) model were varied to study their influence on the predicted RCP values. The performance of all the GEP models was assessed using a variety of performance indices, i.e., R2, RMSE and comparison of regression slopes. The optimal GEP model (Model T3) was obtained when the Nc = 100, Hs = 8 and Ng = 3. This model exhibits an R2 of 0.89 and 0.92 in the training and testing phases, respectively. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R2 values. Similarly, parametric analysis was also conducted for the best performing Model T3. The analysis showed that the amount of binder, compressive strength and age of the sample enhanced the RCP resistance of the concrete specimens. Among the different input variables, the RCP resistance sharply increased during initial stages of curing (28-d), thus validating the model results.",
• notes = "also known as \cite{ma15196959}",

}

Genetic Programming entries for Muhammad Nasir Amin Muhammad Raheel Mudassir Iqbal Kaffayatullah Khan Muhammad Ghulam Qadir Fazal E Jalal Anas Abdulalim Alabdullah Ali Ajwad Majdi Adel Al-Faiad Abdullah Mohammad Abu-Arab

Citations