Multiresolution analysis and deep learning for corroded pipeline failure assessment

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.

Download Full-text

Weld crack assessments in API X65 pipeline: failure assessment diagrams with variations in representative mechanical properties

Materials Science and Engineering A ◽

10.1016/j.msea.2003.12.039 ◽

2004 ◽

Vol 373 (1-2) ◽

pp. 122-130 ◽

Cited By ~ 16

Author(s):

Jung-Suk Lee ◽

Jang-Bog Ju ◽

Jae-il Jang ◽

Woo-Sik Kim ◽

Dongil Kwon

Keyword(s):

Mechanical Properties ◽

Failure Assessment ◽

Pipeline Failure

Download Full-text

Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis

Electronics ◽

10.3390/electronics10151772 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1772

Author(s):

Ahcene Arbaoui ◽

Abdeldjalil Ouahabi ◽

Sébastien Jacques ◽

Madina Hamiane

Keyword(s):

Deep Learning ◽

Multiresolution Analysis ◽

Crack Detection ◽

Concrete Surface ◽

Concrete Material ◽

Open Access Database ◽

Original Procedure ◽

Ultimate Objective ◽

Ultrasonic Investigation ◽

Major Data

In this paper, we propose a new methodology for crack detection and monitoring in concrete structures. This approach is based on a multiresolution analysis of a sample or a specimen of concrete material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processed by a customized wavelet is analyzed at various scales in order to detect internal cracks and crack initiation. The ultimate objective of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible. This is achieved through the combination of two major data analysis tools which are wavelets and deep learning. This original procedure is shown to yield a high accuracy close to 90%. In order to evaluate the performance of the proposed CNN architectures, we also used an open access database, SDNET2018, for the automatic detection of external cracks.

Download Full-text

171 Influence of candesartan on myocardial perfusion measured by use of quantitative myocardial contrast echocardiography (QMCE): A substudy of the candesartan cilexetil in heart failure assessment of reduction in mortality and morbidity (CHARM) -program

European Journal of Echocardiography ◽

10.1016/s1525-2167(99)80103-6 ◽

1999 ◽

Vol 1 ◽

pp. S30-S30

Author(s):

J WUNDERLICH ◽

G BRAND ◽

W MEYERSABELLEK ◽

N POGACIC ◽

H POLZ ◽

...

Keyword(s):

Heart Failure ◽

Myocardial Perfusion ◽

Candesartan Cilexetil ◽

Contrast Echocardiography ◽

Myocardial Contrast Echocardiography ◽

Mortality And Morbidity ◽

Failure Assessment

Download Full-text

Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

Download Full-text

Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Download Full-text