Multiresolution analysis and deep learning for corroded pipeline failure assessment

2021 ◽  
Vol 162-163 ◽  
pp. 103066
Author(s):  
Adriano Dayvson Marques Ferreira ◽  
Silvana M.B. Afonso ◽  
Ramiro B. Willmersdorf ◽  
Paulo R.M. Lyra
Keyword(s):  
Deep Learning ◽  
Multiresolution Analysis ◽  
Failure Assessment ◽  
Pipeline Failure

scholarly journals Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 13099-13111
Author(s):  
Khaled A. Althelaya ◽  
Salahadin A. Mohammed ◽  
El-Sayed M. El-Alfy
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Multiresolution Analysis ◽  
Stock Market Forecasting

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

2021 ◽  
Vol 15 (58) ◽  
pp. 33-47
Author(s):  
Ahcene Arbaoui ◽  
Abdeldjalil Ouahabi ◽  
Sebastien Jacques ◽  
Madina Hamiane
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Multiresolution Analysis ◽  
Early Stage ◽  
Concrete Surface ◽  
Automatic Identification ◽  
Engineering Structures ◽  
Optical Inspection ◽  
Formation Of Cracks ◽  
Non Destructive

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.

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

2004 ◽  
Vol 373 (1-2) ◽  
pp. 122-130 ◽  
Author(s):  
Jung-Suk Lee ◽  
Jang-Bog Ju ◽  
Jae-il Jang ◽  
Woo-Sik Kim ◽  
Dongil Kwon
Keyword(s):  
Mechanical Properties ◽  
Failure Assessment ◽  
Pipeline Failure

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

Electronics ◽  
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.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

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.

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