CNN-based Concrete Cracks Detection Using Multiresolution Analysis

This article proposes an efficient method of auscultation of concrete structures with a non-destructive method using an ultrasound device (Pendit L-200). For the purposes of this work, we have prepared different defects of dosage of the constituents of the concrete, namely sand, different gravels, cement and water, and this with progressively increasing proportions, keeping the others as recommended by the standards. For the defects considered, the transverse intrinsic signals at the center and transversal of the specimens are taken in order to constitute a database. The key element of this method is the multi-resolution analysis based on wavelets. This analysis is coupled with an automatic identification scheme of the types of dosage defects based on deep learning by convolutional neural network (CNN): a technology that is nowadays at the cutting edge of machine learning, especially for all pattern recognition applications.