Multilayer Perceptron Method of Artificial Neural Network in Classifying Concrete Compressive Strength

Abstract

Compressive strength is a basic feature of concrete, it reflects the bearing capacity of concrete. Therefore classifying concrete compressive strength (CCS) plays a vital role. When CCS classification accuracy is improved which will be grounded to calculating bearing capacity, deformation of concrete and reinforced concrete structures better. The objective of this paper is to use multilayer perceptron (MLP) model for classifying CCS. The predictive accuracy of model was compared with several other model including support vector machine (SVM), Navie Bayes (NB) and decision tree (DT). Analytical results showed the MLP model was superior to other comparative models for concrete dataset. Particularly, the MLP was the best model achieving the highest results (92.524% of accuracy). Therefore, MLP model is considered a suitable tool to classify CCS dataset.