Cracked concrete surface image classification on low-dimensional image using artificial intelligence algorithms

The project aims to create a Convolutional neural network (CNN) to detect and classify building cracks. Cracks are a key factor in determining how well-built a concrete structure is since they affect its sturdiness, utility, and safety. Due to its superior image processing capabilities, CNN is rapid...

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Main Author:	Rashid, Rashid Taha Siham
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/99565/1/RashidTahaSihamMSKE2022.pdf
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spelling	my-utm-ep.995652023-03-01T08:16:07Z Cracked concrete surface image classification on low-dimensional image using artificial intelligence algorithms 2022 Rashid, Rashid Taha Siham TK Electrical engineering. Electronics Nuclear engineering The project aims to create a Convolutional neural network (CNN) to detect and classify building cracks. Cracks are a key factor in determining how well-built a concrete structure is since they affect its sturdiness, utility, and safety. Due to its superior image processing capabilities, CNN is rapidly gaining traction as a credible option to replace manual crack detection. Cracks on the concrete surface are one of the earliest signs of structural damage, which is important for maintenance and can cause significant environmental harm. The first step in a manual examination is to sketch the crack and note the conditions. The manual approach is dependent on the specialist’s expertise and experience, resulting in a lack of impartiality in quantitative analysis. As an alternative, automated image-based crack detection is suggested where a variety of detection methods are available, such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees (DT), artificial neural networks (ANN), and convolutional neural networks (CNN). These techniques will be used in this project. Positive crack and negative crack are two classes that make up the dataset that will be used with the mentioned strategies, and there are 20,000 photos per class. The images are resized into five different sizes (50×50, 35×35, 25×25, 10×10, and 5×5), and then the results are analyzed based on the performance of the techniques used in the project. It is concluded that the performance with low-resolution images is at par with that of high-resolution images. In addition, for the 50×50 sample image, the accuracy score of the classifiers (KNN, SVM, DT, ANN, and CNN) was (89, 98, 97, 94, and 99) % respectively, while for the 5×5 sample image, the value of the accuracy was (91, 90, 89, 92 and 95) % respectively. 2022 Thesis http://eprints.utm.my/id/eprint/99565/ http://eprints.utm.my/id/eprint/99565/1/RashidTahaSihamMSKE2022.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149745 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Rashid, Rashid Taha Siham Cracked concrete surface image classification on low-dimensional image using artificial intelligence algorithms
description	The project aims to create a Convolutional neural network (CNN) to detect and classify building cracks. Cracks are a key factor in determining how well-built a concrete structure is since they affect its sturdiness, utility, and safety. Due to its superior image processing capabilities, CNN is rapidly gaining traction as a credible option to replace manual crack detection. Cracks on the concrete surface are one of the earliest signs of structural damage, which is important for maintenance and can cause significant environmental harm. The first step in a manual examination is to sketch the crack and note the conditions. The manual approach is dependent on the specialist’s expertise and experience, resulting in a lack of impartiality in quantitative analysis. As an alternative, automated image-based crack detection is suggested where a variety of detection methods are available, such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees (DT), artificial neural networks (ANN), and convolutional neural networks (CNN). These techniques will be used in this project. Positive crack and negative crack are two classes that make up the dataset that will be used with the mentioned strategies, and there are 20,000 photos per class. The images are resized into five different sizes (50×50, 35×35, 25×25, 10×10, and 5×5), and then the results are analyzed based on the performance of the techniques used in the project. It is concluded that the performance with low-resolution images is at par with that of high-resolution images. In addition, for the 50×50 sample image, the accuracy score of the classifiers (KNN, SVM, DT, ANN, and CNN) was (89, 98, 97, 94, and 99) % respectively, while for the 5×5 sample image, the value of the accuracy was (91, 90, 89, 92 and 95) % respectively.
format	Thesis
qualification_level	Master's degree
author	Rashid, Rashid Taha Siham
author_facet	Rashid, Rashid Taha Siham
author_sort	Rashid, Rashid Taha Siham
title	Cracked concrete surface image classification on low-dimensional image using artificial intelligence algorithms
title_short	Cracked concrete surface image classification on low-dimensional image using artificial intelligence algorithms
title_full	Cracked concrete surface image classification on low-dimensional image using artificial intelligence algorithms
title_fullStr	Cracked concrete surface image classification on low-dimensional image using artificial intelligence algorithms
title_full_unstemmed	Cracked concrete surface image classification on low-dimensional image using artificial intelligence algorithms
title_sort	cracked concrete surface image classification on low-dimensional image using artificial intelligence algorithms
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2022
url	http://eprints.utm.my/id/eprint/99565/1/RashidTahaSihamMSKE2022.pdf
_version_	1776100618445783040

Cracked concrete surface image classification on low-dimensional image using artificial intelligence algorithms

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