Pavement Crack Identification Based on Deep Learning and Denoising Model

Crack identification in thick beams has improved increasing considerations from the scientific and building areas since the unpredicted structural failure may cause disastrous, catastrophic and life trouble. The goal of the present examination is to predict the unknown crack location and its depth in thick beams from the information of frequency data obtained from experimental examination. The effectiveness of the proposed strategy is approved by numerical simulations in view of experimental data for a cantilever beam, free-free beam and simply supported beam. With the improvements in delicate figuring, optimization strategies are acknowledged to be an extremely proficient instrument to offer an answer for crack identification issue. In the simulation modeling, the parameters, for example, shift; modal assurance criterion (MAC) and stiffness, are predicted by utilizing optimized deep learning neural network (ODNN) approach in view of crack location and size. To improve the weight in DLNN, the opposition-based ant lion (OAL) is used by minimizing the mean square error (MSE) rate. The result shows that the proposed model achieves the optimal performance compared with existing techniques.

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Vision Measurement of Tunnel Structures with Robust Modelling and Deep Learning Algorithms

Sensors ◽

10.3390/s20174945 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4945 ◽

Cited By ~ 5

Author(s):

Xiangyang Xu ◽

Hao Yang

Keyword(s):

Deep Learning ◽

Health Monitoring ◽

Crack Detection ◽

Three Dimensional ◽

Image Data ◽

Measurement Unit ◽

Crack Identification ◽

Monitoring Method ◽

Vision Measurement ◽

Robust Modelling

The health monitoring of tunnel structures is vital to the safe operation of railway transportation systems. With the increasing mileage of tunnels, regular inspection and health monitoring are urgently demanded for the tunnel structures, especially for information regarding deformation and damage. However, traditional methods of tunnel inspection are time-consuming, expensive and highly dependent on human subjectivity. In this paper, an automatic tunnel monitoring method is investigated based on image data which is collected through the moving vision measurement unit consisting of camera array. Furthermore, geometric modelling and crack inspection algorithms are proposed where a robust three-dimensional tunnel model is reconstructed utilizing a B-spline method and crack identification is conducted by means of a Mask R-CNN network. The innovation of this investigation is that we combine the robust modelling which could be applied for the deformation analysis and the crack detection where a deep learning method is employed to recognize the tunnel cracks intelligently based on image sensors. In this study, experiments were conducted on a subway tunnel structure several kilometers long, and a robust three-dimensional model is generated and the cracks are identified automatically with the image data. The superiority of this proposal is that the comprehensive information of geometry deformation and crack damage can ensure the reliability and improve the accuracy of health monitoring.

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Deep learning–based autonomous concrete crack evaluation through hybrid image scanning

Structural Health Monitoring ◽

10.1177/1475921718821719 ◽

2019 ◽

Vol 18 (5-6) ◽

pp. 1722-1737 ◽

Cited By ~ 21

Author(s):

Keunyoung Jang ◽

Namgyu Kim ◽

Yun-Kyu An

Keyword(s):

Decision Making ◽

Deep Learning ◽

Large Scale ◽

Crack Detection ◽

Continuous Wave ◽

Imaging System ◽

Concrete Specimen ◽

Crack Identification ◽

False Alarms ◽

Concrete Crack

This article proposes a deep learning–based autonomous concrete crack detection technique using hybrid images. The hybrid images combining vision and infrared thermography images are able to improve crack detectability while minimizing false alarms. In particular, large-scale concrete-made infrastructures such as bridge and dam can be effectively inspected by spatially scanning the unmanned vehicle–mounted hybrid imaging system including a vision camera, an infrared camera, and a continuous-wave line laser. However, the expert-dependent decision-making for crack identification which has been widely used in industrial fields is often cumbersome, time-consuming, and unreliable. As a target concrete structure gets larger, automated decision-making becomes more desirable from the practical point of view. The proposed technique is able to achieve automated crack identification and visualization by transfer learning of a well-trained deep convolutional neural network, that is, GoogLeNet, while retaining the advantages of the hybrid images. The proposed technique is experimentally validated using a lab-scale concrete specimen with cracks of various sizes. The test results reveal that macro- and microcracks are automatically visualized while minimizing false alarms.

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Deep Learning-Based Crack Identification for Steel Pipelines by Extracting Features from 3D Shadow Modeling

Applied Sciences ◽

10.3390/app11136063 ◽

2021 ◽

Vol 11 (13) ◽

pp. 6063

Author(s):

Wael A. Altabey ◽

Mohammad Noori ◽

Tianyu Wang ◽

Ramin Ghiasi ◽

Sin-Chi Kuok ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Three Dimensional ◽

Crack Identification ◽

Automatic Identification ◽

Light Sources ◽

3D Images ◽

Deep Learning Algorithm ◽

Satisfactory Performance ◽

A New Technique

Automatic crack identification for pipeline analysis utilizes three-dimensional (3D) image technology to improve the accuracy and reliability of crack identification. A new technique that integrates a deep learning algorithm and 3D shadow modeling (3D-SM) is proposed for the automatic identification of corrosion cracks in pipelines. Since the depth of a corrosion crack is below the surrounding area of the crack, a shadow of the crack is projected when the crack is exposed under light sources. In this study, we analyze the shadow areas of cracks through 3D shadow modeling (3D-SM) and identify the evolving cracks through the shape analysis of the shadows. To denoise the 3D images, the connected domain analysis is implemented so that the shadow groups of the evolving cracks can be retained and the scattered shadow groups that occur due to insignificant defects can be eliminated. Moreover, a novel deep neural network is developed to process the 3D images. The proposed automatic crack identification method successfully processes the 3D images efficiently and accurately diagnoses the corrosion cracks. Experimental results show that the proposed method achieves satisfactory performance with 93.53% accuracy and a 92.04% regression rate.

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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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.

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