Concrete compressive strength detection using image processing based new test method

Today, Artificial Neural Networks (ANN) and Image Processing (IP) are particularly used to solve engineering problems. This study uses ANN and IP together to determine the mechanical properties of concrete, such as the compressive strength, modulus of elasticity and maximum deformation, at a certain success rate. In other words, the primary objective of study is to predict the mechanical properties of concrete without causing destruction, using a new alternative method. In this context, using five distinctive parameters (water/cement ratio, curing, amount of cement, compression and additive), 96 cylindrical concrete samples were produced; images of the samples were taken before they were examined at the compression testing, and the training and testing procedures for ANN and IP were realized using the obtained pressure readings at the laboratory. In addition to 96 cylindrical concrete samples, 48 were randomly selected to verify ANN and IP. From both the training/test samples and the verification samples, there is a notably high correlation between the outcomes of ANN and IP and the actual results, which varies between 97.18% and 99.87%. When ANN and IP were used together, the described method is a good alternative to the traditional destructive and nondestructive methods that are currently used to identify the mechanical properties of concrete.