Wavelet-based multiresolution analysis coupled with deep learning to efficiently monitor cracks in concrete

Abdeldjalil Ouahabi,Ahcene Arbaoui,Madina Hamiane,Sebastien Jacques

doi:10.3221/igf-esis.58.03

Abstract

This paper proposes an efficient methodology to monitor the formation of cracks in concrete after non-destructive ultrasonic testing of a structure. The objective is to be able to automatically detect the initiation of cracks early enough, i.e. well before they are visible on the concrete surface, in order to implement adequate maintenance actions on civil engineering structures. The key element of this original approach is the wavelet-based multiresolution analysis of the ultrasonic signal received from a sample or a specimen of the studied material subjected to several types of solicitation. This analysis is finally coupled to an automatic identification scheme of the types of cracks based on artificial neural networks (ANNs), and in particular deep learning by convolutional neural networks (CNNs); a technology today at the cutting edge of machine learning, in particular for all applications of pattern recognition. Wavelet-based multiresolution analysis does not add any value in detecting fractures in concrete visible by optical inspection. However, the results of its implementation coupled with different CNN architectures show cracks in concrete can be identified at an early stage with a very high accuracy, i.e. around 99.8%, and a loss function of less than 0.1, regardless of the implemented learning architecture.

Highlights

Since its creation, concrete, the flagship material of civil engineering structures, has been the basis of construction techniques, whether industrial or hydraulic, and of infrastructures such as transportation or urban infrastructure
The service life of reinforced concrete structures is conditioned by the response to chemical, physical and mechanical aggressions of the environment, as well as by the capacity of the constituent materials to protect themselves against these aggressions
We tested the methodology on available image datasets of visually or optically observable cracks on the surface of concrete samples

Summary

Introduction

Concrete, the flagship material of civil engineering structures, has been the basis of construction techniques, whether industrial (factories, warehouses, ...) or hydraulic (dams, dikes, ...), and of infrastructures such as transportation (bridges, tunnels, ...) or urban infrastructure (aqueducts, ...). This success is due to several factors: concrete is an economical material, easy to work with, resistant to compressive stress, durable, sound and heat insulating, and it contributes to architecture through the shapes, textures and colors it provides [1]. This research topic is still of great interest and recent studies have shown that it is possible to limit interfacial micro-cracks in concrete and its composites subjected to dynamic loads, for instance by adding fly ash and/or silica fume at a rate of a few thenths of the weight of the cement [7, 8]

Objectives

Methods

Results