Modelling the strength of lightweight foamed concrete using support vector machine (SVM)

Abstract Strength of concrete is a primary criterion in selecting this material for a particular application. This construction material gains strength over a long period of time after pouring. Characteristic strength of normal concrete that considered in structural design is defined as the compressive strength of a sample that has been aged for 28 days. Rapid and reliable prediction for the strength of concrete would be economically and practically of great significance. Therefore; the prediction of concrete strength has been an active area of research and a considerable number of studies have been carried out. In this study, two techniques were used to propose a model which is capable of predicting the compressive strength with acceptable accuracy, these were the revolutionary support vector machine (SVM) and the multivariable non-linear regression. Support vector machine model was proposed and developed for the prediction of concrete compressive strength at early age. The variables used in the prediction models were from the knowledge of the mix proportion elements and 7-day compressive strength. The models provide good estimation of compressive strength and yielded good correlations with the data used in this study relative to nonlinear multivariable regression. Moreover, the SVM model proved to be significant tool in prediction compressive strength of lightweight foamed concrete with minimal mean square errors and standard deviation.