Volume 23, Issue 5 (2023)                   MCEJ 2023, 23(5): 21-34 | Back to browse issues page

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Seifollahi M, Abbasi S, Fahimi Farzam M, Daneshfaraz R. Crack Detection in Concrete Structures Using Convolutional Neural Network. MCEJ 2023; 23 (5) :21-34
URL: http://mcej.modares.ac.ir/article-16-54192-en.html
1- M.Sc., Graduated of Civil-Hydraulic Structures Eng., Faculty of Civil Eng., Univ. of Tabriz, Tabriz, Iran
2- M.Sc., Graduated of Civil-Hydraulic Structures Eng., Faculty of Eng., Univ. of Mohaghegh Ardabili, Ardabil, Iran
3- Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh, Iran , m.farzam@maragheh.ac.ir
4- Professor of Civil Eng., Faculty of Eng., Univ. of Maragheh, Maragheh, Iran
Abstract:   (2138 Views)
One of the active areas of research in concrete structure health monitoring is the detection of cracking in structural elements. Image classification and diagnosis have attracted the attention of many researchers nowadays. Due to the advancement of artificial neural networks and their fast processing, a convolution neural network has been established to detect these cracks. In this study, crack detection in concrete structures has been studied using a convolutional neural network, which can be generalized to all concrete structures for example dams, canals, bridges, shells, road infrastructure, foundations and concrete frames. Convolution neural network training was performed by the SGDM method with the ReLU activator function. Also, 250 iterations were employed for convolution neural network training, which gradually reduced the error rate and increased the accuracy of detecting cracked and uneaten concrete. The convolutional neural network is trained and validated with these 250 iterations. First, images with 32-pixel window dimensions are converted and separated. Then, the 32-pixel window, the 16-pixel, and the 8-pixel windows filter the images. A total of 3 stages of 32, 16, and 8-pixel filter images are analyzed and interpreted. During the training process, validation is performed every 20 iterations, and a diagram related to the accuracy of convolution network estimation and data classification error is drawn and completed. In convolutional neural networks, where the output is in pairs, the cracked and uncracked images of the network architecture are almost identical, differing only in minor specifications. The database of this research includes 20,000 images of cracked concrete and 20,000 uncracked concrete with dimensions of 3×227×227 pixels, 80% of it is used for training and the remaining 20% is used for validation of the convolution neural network. The accuracy of distinguishing cracked concrete from uncracked ones is about 98.16%, which is acceptable for operation and is considered practical. To evaluate the accuracy and performance of the proposed algorithm, each classification was performed against the overall accuracy, the confusion matrix was used for the validation data. According to the clutter matrix, 3861 images, in other words, 48.3% have been predicted to be correctly cracked, and 3992 images, equivalent to 49.9%, have been predicted to be correctly uncracked, and a total of 147 incorrect images have been predicted, which is equivalent to 1.8 percent. Images that are cracked and not accidentally cracked are predicted. They had crack lines in the corner of the image or cracks with a very small width, which the proposed convolutional neural network was mistaken for due to a very small crack width or crack position. Also, the results of the present study showed that the accuracy of this research has the best accuracy in less analysis time compared to previous studies. It should be noted that this method and its associated database can be used to produce a crack detection application on a smartphone, to be able to make a good initial estimate of the structure in question, such as a bridge or building after an unusual loading event, such as an earthquake or explosion.
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Article Type: Original Research | Subject: Civil and Structural Engineering
Received: 2021/07/19 | Accepted: 2022/03/6 | Published: 2022/05/31

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