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A platform for research: civil engineering, architecture and urbanism
Crack Detection in Concrete Using Artificial Neural Networks
This paper aims to explore the possibility of using machine learning (ML) algorithms and image processing to determine cracks in concrete and classify them as Cracked and Uncracked. This is a very current field of study with a lot of research currently taking place. In particular, neural network algorithms such as VGG16, ResNet50, Xception and MobileNet, were used to name a few. Two datasets were used to detect the presence of cracks in concrete. The first two datasets were taken from the Kaggle website. The first dataset is generated from 458 high-resolution images (4032 × 3024 pixels). This dataset consists of 40,000 images, 20,000 with and 20,000 without cracks. The second dataset had pictures of cracked and uncracked decks on a bridge from a dataset called SDNET2018 (2018). VGG16 Architecture based artificial neural network performed the best while MobileNet performed the worst. As the scope for the project expanded, an effort was made to determine crack properties, specifically crack width as an automated system for the same would be much more useful than a manual one. It was done using morphological transformations and concepts of Euclidean distance.
Crack Detection in Concrete Using Artificial Neural Networks
This paper aims to explore the possibility of using machine learning (ML) algorithms and image processing to determine cracks in concrete and classify them as Cracked and Uncracked. This is a very current field of study with a lot of research currently taking place. In particular, neural network algorithms such as VGG16, ResNet50, Xception and MobileNet, were used to name a few. Two datasets were used to detect the presence of cracks in concrete. The first two datasets were taken from the Kaggle website. The first dataset is generated from 458 high-resolution images (4032 × 3024 pixels). This dataset consists of 40,000 images, 20,000 with and 20,000 without cracks. The second dataset had pictures of cracked and uncracked decks on a bridge from a dataset called SDNET2018 (2018). VGG16 Architecture based artificial neural network performed the best while MobileNet performed the worst. As the scope for the project expanded, an effort was made to determine crack properties, specifically crack width as an automated system for the same would be much more useful than a manual one. It was done using morphological transformations and concepts of Euclidean distance.
Crack Detection in Concrete Using Artificial Neural Networks
Lecture Notes in Civil Engineering
Marano, Giuseppe Carlo (editor) / Rahul, A. V. (editor) / Antony, Jiji (editor) / Unni Kartha, G. (editor) / Kavitha, P. E. (editor) / Preethi, M. (editor) / Palanisamy, T. (author) / Shakya, Rajat (author) / Nalla, Sudeepthi (author) / Prakhya, Sai Shruti (author)
International Conference on Structural Engineering and Construction Management ; 2022 ; Angamaly, India
2022-10-30
9 pages
Article/Chapter (Book)
Electronic Resource
English
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