Predicting compressive strength of CRM samples using Image processing and ANN

Abstract

Quality of concrete is majorly ascertained through its compressive strength which has a significant role in the stability of concrete structures. In this study, artificial neural network (ANN) and image processing (IP) techniques were used to predict the concrete compressive strength (fc ) with cement replacement material (CRM), i.e., Fly Ash (FA) and Silica Fumes (SF). 18 concrete cylinders were cast with different mix ratios and with different % of CRM. Half of them were tested for compression strength in the laboratory and remaining cylinders were cut into three slices each, for prediction of compressive strength through the proposed technique. Images were obtained using a DSLR camera under defined conditions to extract the features. Based on the extracted features, ANN modelling was performed for predicting fc . A comparison of experimental results and ANN results (R = 0.9865) proved ANN models can be used as a prediction tool for compressive strength of concrete.