Prediction of the compressive strength of vacuum processed concretes using artificial neural network and regression techniques

Abstract

Concrete which is a composite material is one of the most important construction materials. For the improvement of concrete quality some advanced technologies are used for curing and placement of concrete. Vacuum processing is one of these technologies. With the vacuum application, water content of the mixture is decreased and by this way a better water/cement ratio is obtained. Since most of the empirical equations which use nondestructive test results are developed for normal concretes, their prediction performance for vacuum processed concrete is unclear. In this study regression equations and an artificial neural network (ANN) were developed for the estimation of compressive strength of vacuum processed concrete. For the experimental set up, three different concretes were prepared by applying variable vacuum application duration. On these concrete samples, Windsor probe penetration tests, Schmidt hammer tests, pulse velocity determination tests, were performed. In addition to these; densities, void ratios, water absorption values and capillary water absorption values of extracted core samples were determined. Several equations using single independent variables for the estimation of compressive strength were developed, a multi linear regression equation which uses Windsor probe exposed length, pulse velocity, density and water absorption ratio as predictor variables was developed. A neural network was developed for the estimation of compressive strength. Finally prediction performances of previously published empirical equations, single and multiple variable regression equations developed during this study and ANN were compared. According to this comparison, best prediction performance belongs to ANN. Key words: Artificial neural network, vacuum processed concrete, nondestructive testing, statistical analysis.