Evaluation of concrete compressive strength based on an improved PSO-LSSVM model

This paper investigates the potential of a hybrid model which combines the least squares support vector machine (LSSVM) and an improved particle swarm optimization (IMPSO) techniques for prediction of concrete compressive strength. A modified PSO algorithm is employed in determining the optimal values of LSSVM parameters to improve the forecasting accuracy. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed IMPSO-LSSVM model. Further, predictions from five models (the IMPSO-LSSVM, PSOLSSVM, genetic algorithm (GA) based LSSVM, back propagation (BP) neural network, and a statistical model) were compared with the experimental data. The results show that the proposed IMPSO-LSSVM model is a feasible and efficient tool for predicting the concrete compressive strength with high accuracy.