Monitoring and analysis of reinforced concrete plate-column structure under room temperature and fire based on acoustic emission

Wang, Dongye; Dong, Yinmao; Zhang, Dashan; Wang, Weihua; Lu, Xin

doi:10.3221/igf-esis.53.20

Cited by 3 publications

(1 citation statement)

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“…More precisely, Bhaskar et al showed that when the porous zone at the interface between steel and concrete (a zone whose volume depends on the surface of the reinforcement, the water/cement mass ratio and the degree of hydration) is completely filled by the corrosion products, pressures are exerted on the concrete cover and can generate cracks [3]. As shown in Figure 1, monitoring the propagation of these cracks is therefore essential to stop them from reaching critical sizes that could lead to the reduction of the bearing capacity of the concrete or reinforced concrete structure or even to its failure [4][5][6][7][8]. 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].…”

Section: Introductionmentioning

confidence: 99%

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

Arbaoui

Ouahabi

Jacques

et al. 2021

Frattura Ed Integrità Strutturale

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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.

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