Inspection, Identification and Repair Monitoring of Cracked Concrete structure –An application of Image processing

Meghana, R K; Apoorva, SM; Mohana,; Chitkara, Yojan

doi:10.1109/cesys.2018.8723898

2018 3rd International Conference on Communication and Electronics Systems (ICCES) 2018

DOI: 10.1109/cesys.2018.8723898

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Inspection, Identification and Repair Monitoring of Cracked Concrete structure –An application of Image processing

R K Meghana¹,

SM Apoorva²,

Mohana Mohana³

et al.

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Cited by 2 publications

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Concrete Bridge Crack Image Classification Using Histograms of Oriented Gradients, Uniform Local Binary Patterns, and Kernel Principal Component Analysis

et al. 2022

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Bridges deteriorate over time, which requires the continuous monitoring of their condition. There are many digital technologies for inspecting and monitoring bridges in real-time. In this context, computer vision has extensively studied cracks to automate their identification in concrete surfaces, overcoming the conventional manual methods that rely on human judgment. The general framework of vision-based techniques consists of feature extraction using different filters and descriptors and classifier training to perform the classification task. However, training can be time-consuming and computationally expensive, depending on the dimension of the features. To address this limitation, dimensionality reduction techniques are applied to extracted features, and a new feature subspace is generated. This work used histograms of oriented gradients (HOGs) and uniform local binary patterns (ULBPs) to extract features from a dataset containing over 3000 uncracked and cracked images covering different patterns of cracks and concrete surface representations. Nonlinear dimensionality reduction was performed using kernel principal component analysis (KPCA), and three machine learning classifiers were implemented to conduct the classification. The experimental results show that the classification scheme based on the support-vector machine (SVM) model and feature-level fusion of the HOG and ULBP features after KPCA application provided the best results as an accuracy of 99.26% was achieved by the proposed classification framework.

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Concrete Bridge Crack Image Classification Using Histograms of Oriented Gradients, Uniform Local Binary Patterns, and Kernel Principal Component Analysis

et al. 2022

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Composite Polymer Coatings for Repair of Concrete Linings of Channels

Baev

Kosichenko

Garbuz

2021

DDF

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The paper is dedicated to the results of research on composite polymer material used for concrete casings repair. Field approbation of new material was carried out on the irrigation channel test site during the repair of concrete coating deformation joints and cracks. The paper provides the obtained theoretical formulae and results of water permeability calculation in concrete casing cracks with smooth and rough walls. A design chart for determining water permeability of damages in the form of cracks of various coating width on channel concrete coatings was obtained. A universal nomogram was obtained for determining the water permeability of damage in the form of cracks of various opening widths on the concrete coverings of channels.

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Inspection, Identification and Repair Monitoring of Cracked Concrete structure –An application of Image processing

Cited by 2 publications

References 4 publications

Concrete Bridge Crack Image Classification Using Histograms of Oriented Gradients, Uniform Local Binary Patterns, and Kernel Principal Component Analysis

Concrete Bridge Crack Image Classification Using Histograms of Oriented Gradients, Uniform Local Binary Patterns, and Kernel Principal Component Analysis

Composite Polymer Coatings for Repair of Concrete Linings of Channels

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