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.

scholarly journals Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis

2021 ◽  
Author(s):  
Ahcene Arbaoui ◽  
Abdeldjalil Ouahabi ◽  
Sébastien Jacques ◽  
Madina Hamiane
Keyword(s):  
Deep Learning ◽  
Concrete Structures ◽  
Concrete Surface ◽  
Multi Resolution Analysis ◽  
Crack Monitoring ◽  
Crack Type ◽  
Resolution Analysis ◽  
Original Procedure ◽  
Ultrasonic Investigation ◽  
Major Data

In this paper, we propose a new methodology for crack monitoring in concrete structures. This approach is based on a n this paper, we propose a new methodology for monitoring cracks in concrete structures. This approach is based on a multi-resolution analysis of a sample or a specimen of the studied material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processing by a dedicated wavelet will be analyzed according to several scales in order to detect internal cracks and crack initiation. The ultimate goal 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 on the concrete surface. The key idea allowing such a performance is the combination of two major data analysis tools which are wavelets and Deep Learning. This original procedure allows to reach a high accuracy close to 0.90. In this work, we have also implemented another approach for automatic detection of external cracks by deep learning from publicly available datasets.

scholarly journals DEEP LEARNING AND RANDOM FOREST BASED CRACK DETECTION FROM AN IMAGE OF CONCRETE SURFACE

2017 ◽  
Vol 73 (2) ◽  
pp. I_297-I_307 ◽  
Author(s):  
Pang-jo CHUN ◽  
Yuri SHIMAMOTO ◽  
Kazuaki OKUBO ◽  
Chihiro MIWA ◽  
Mitao OHGA
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Crack Detection ◽  
Concrete Surface

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.

scholarly journals Autonomous Crack and Bughole Detection for Concrete Surface Image Based on Deep Learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yujia Sun ◽  
Yang Yang ◽  
Gang Yao ◽  
Fujia Wei ◽  
Mingpu Wong
Keyword(s):  
Deep Learning ◽  
Concrete Surface ◽  
Surface Image

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

2021 ◽  
pp. 136943322098663
Author(s):  
Diana Andrushia A ◽  
Anand N ◽  
Eva Lubloy ◽  
Prince Arulraj G
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Method ◽  
Concrete Structures ◽  
Vital Role ◽  
Subjective Knowledge ◽  
Thermal Cracks ◽  
Reinforced Cement ◽  
Novel Method ◽  
Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

scholarly journals LiNbO3Coating on Concrete Surface: A New and Environmentally Friendly Route for Artificial Photosynthesis

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Ranjit K. Nath ◽  
M. F. M. Zain ◽  
Abdul Amir H. Kadhum
Keyword(s):  
Building Materials ◽  
Artificial Photosynthesis ◽  
Reaction Pathway ◽  
Research Work ◽  
Pollution Prevention ◽  
Environmentally Friendly ◽  
Concrete Surface ◽  
Concrete Material ◽  
Gas Phases ◽  
Work Done

The addition of a photocatalyst to ordinary building materials such as concrete creates environmentally friendly materials by which air pollution or pollution of the surface can be diminished. The use of LiNbO3photocatalyst in concrete material would be more beneficial since it can produce artificial photosynthesis in concrete. In these research photoassisted solid-gas phases reduction of carbon dioxide (artificial photosynthesis) was performed using a photocatalyst, LiNbO3, coated on concrete surface under illumination of UV-visible or sunlight and showed that LiNbO3achieved high conversion of CO2into products despite the low levels of band-gap light available. The high reaction efficiency of LiNbO3is explained by its strong remnant polarization (70 µC/cm2), allowing a longer lifetime of photoinduced carriers as well as an alternative reaction pathway. Due to the ease of usage and good photocatalytic efficiency, the research work done showed its potential application in pollution prevention.

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