Data-driven model for ternary-blend concrete compressive strength prediction using machine learning approach

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@Article{SALAMI:2021:CBM,

• author = "Babatunde Abiodun Salami and Teslim Olayiwola and Tajudeen A. Oyehan and Ishaq A. Raji",
• title = "Data-driven model for ternary-blend concrete compressive strength prediction using machine learning approach",
• journal = "Construction and Building Materials",
• volume = "301",
• pages = "124152",
• year = "2021",
• ISSN = "0950-0618",
• DOI = "doi:10.1016/j.conbuildmat.2021.124152",
• URL = "https://www.sciencedirect.com/science/article/pii/S0950061821019127",
• keywords = "genetic algorithms, genetic programming, Ternary concrete, Blast furnace slag, Fly ash, Compressive strength, Least square support vector machine, Coupled simulated annealing, CSA, LSSVM-CSA, GP",
• abstract = "Ternary-blend concrete is a complex composite material, and the nonlinearity in its compressive strength behavior is unquestionable. Entirely many models have been developed to accurately predict the ternary-blend concrete compressive strength, such as ANN, SVM, random forest, decision tree, to mention but a few. This study underscores the better predictive performance and successful application of the least square support vector machine (LSSVM), a machine learning model for predicting the compressive strength of ternary-blend concrete. Coupled simulated annealing (CSA) was applied to the LSSVM model as an optimization algorithm. In addition, the genetic programming (GP) model was used as a benchmark model to compare the performance of the LSSVM-CSA model. The predictive performance of the LSSVM-CSA was compared with that of some of the proposed models in well-known studies where the same datasets were used. The model proposed in this study outperformed other studies, yielding an R2 value of 0.954",

}

Genetic Programming entries for Babatunde Abiodun Salami Teslim Olayiwola Tajudeen A Oyehan Ishaq A Raji

Citations