Classification of Aggregates Using Basic Shape Parameters Through Neural Networks

In this paper, the aim is to classify natural or crushed aggregates by using concrete and asphalt mixes through artificial neural networks. for classification, it was a used the feature vector which was calculated by using digital image processing techniques. of the five different type coarse aggregates images were taken with 45o and 90o by a 10 mp (sony dsc-r1) and 7.1 mp (canon eos 350d) camera. aggregates images were processed and analyzed by using matlab image processing and neural network toolbox. classification process was made with totally 18 feature vectors, which is 9 vectors each angles, by neural network. results showed image processing and neural networks which are important methods for founding shape parameters and classification of aggregates, and performance, cost and time consuming factors of automation systems in aggregate sources will be effective with these methods.