Journal
CONSTRUCTION AND BUILDING MATERIALS
Volume 301, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.conbuildmat.2021.124152
Keywords
Ternary concrete; Blast furnace slag; Fly ash; Compressive strength; Least square support vector machine; Coupled simulated annealing; CSA; LSSVM-CSA; Genetic programming; GP
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Funding
- Center for Engineering Research, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
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This study presents a model using least square support vector machine to predict the compressive strength of ternary-blend concrete, achieving better predictive performance through coupling simulated annealing as an optimization algorithm.
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 LSSVMCSA 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.
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