Abstract. High-Performance Concrete (HPC) is a complex composite material with highly nonlinear mechanical behavior. Concrete compressive strength, as one of the most essential qualities of concrete, is also a highly nonlinear function of ingredients. In this paper, Least Square Support Vector Regression (LSSVR) model based on Coupled Simulated Annealing (CSA) has been successfully used to nd the nonlinear relationship between the concrete compressive strength and eight input factors (the cement, the blast furnace slags, the y ashes, the water, the superplasticizer, the coarse aggregates, the ne aggregates, age of testing). To evaluate the performance of the CSA-LSSVR model, the results of the hybrid model were compared with those obtained by Arti cial Neural Network (ANN) model. A comparison study is made using the coe cient of determination R 2 and Root Mean Squared Error (RMSE) as evaluation criteria. The accuracy, the computational time, the advantages and shortcomings of these modeling methods are also discussed. The training and testing results have shown that ANNs and CSA-LSSVR models have strong potential for predicting the compressive strength of HPC.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.