Compressive strength prediction of SCC containing fly ash using SVM and PSO-SVM models

Abstract

Self-Compacting Concrete (SCC), is a highly workable material, compacted by its self weight without observable segregation and bleeding. In this study, Support Vector Machine (SVM) and particle swarm optimization based SVM models are employed to predict the 28 days compressive strength of individual SCC mix. A database of 62 no’s of SCC compressive strength from literature with cement partially replaced by fly ash is used for training the models. The test data consists of two groups, an individual study consisting of 9 datasets and other combination of three studies with 19 datasets tested separately. Similar input parameters from the train data is extended for testing the models prediction accuracy. Statistical parameters such as correlation coefficient, root mean square error and scatter index are used to evaluate the models’ prediction results. The particle swarm optimization based SVM model is capable of selecting appropriate SVM parameters to increase the prediction accuracy. From the results, it is seen that both SVM and particle swarm optimized SVM models have good capability in predicting the SCC compressive strength.