Classification and quantification of cracks in concrete structures using deep learning image-based techniques

Flah, Majdi; Suleiman, Ahmed R.; Nehdi, Moncef L.

doi:10.1016/j.cemconcomp.2020.103781

Cited by 121 publications

(53 citation statements)

References 16 publications

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Section: Methodsmentioning

confidence: 99%

“…e kernel slid on the image with the step size of "stride" [8]. e pooling layer helped reduce the spatial dimension of the representation, which can lower the number of iterations, computation size, and weight [8]. Several pooling options are currently available, namely, the average of the rectangular neighborhood, L2 norm of the rectangular neighborhood, and max pooling, which can obtain the maximum value from the subarrays of the input array.…”

Section: Methodsmentioning

confidence: 99%

“…en, the most likely classification was identified by the softmax layer. In the most likely classification, the class had the highest probability [8]. e probability distribution with a sum of one was output.…”

Section: Methodsmentioning

confidence: 99%

“…Using this information, appropriate maintenance measures can be arranged to prevent pavement failure at an early stage. However, manual inspections of asphalt pavement require massive time involvement and labor costs, and the results are not accurate [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Attention‐Based Convolutional Neural Network for Pavement Crack Detection

Wan

Gao

et al. 2021

Advances in Materials Science and Engineering

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Achieving high detection accuracy of pavement cracks with complex textures under different lighting conditions is still challenging. In this context, an encoder-decoder network-based architecture named CrackResAttentionNet was proposed in this study, and the position attention module and channel attention module were connected after each encoder to summarize remote contextual information. The experiment results demonstrated that, compared with other popular models (ENet, ExFuse, FCN, LinkNet, SegNet, and UNet), for the public dataset, CrackResAttentionNet with BCE loss function and PRelu activation function achieved the best performance in terms of precision (89.40), mean IoU (71.51), recall (81.09), and F1 (85.04). Meanwhile, for a self-developed dataset (Yantai dataset), CrackResAttentionNet with BCE loss function and PRelu activation function also had better performance in terms of precision (96.17), mean IoU (83.69), recall (93.44), and F1 (94.79). In particular, for the public dataset, the precision of BCE loss and PRelu activation function was improved by 3.21. For the Yantai dataset, the results indicated that the precision was improved by 0.99, the mean IoU was increased by 0.74, the recall was increased by 1.1, and the F1 for BCE loss and PRelu activation function was increased by 1.24.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Attention‐Based Convolutional Neural Network for Pavement Crack Detection

Wan

Gao

et al. 2021

Advances in Materials Science and Engineering

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“…For instance, more than 100,000 bridges in the United States have early age deck cracks [7]. Besides, among the 570,000 bridges in the USA, 40% were listed as deficient, requiring repair or reconstruction according to the requirements of the Federal Highway Administration (FHWA), with an expected cost of 50 billion dollars [8,9]. More than 510,000 new bridges have been constructed in China over the last 40 years, 15% of which have reached their service life earlier due to cracks.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Detection Accuracy of Concrete Cracks Using Various CNN Models

Mohammed

Han

2021

Advances in Materials Science and Engineering

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Automatic crack detection with the least amount of workforce has become a crucial task in the inspection and evaluation of the performances of concrete structure in civil engineering. Recently, although many concrete crack detection models based on convolutional neural networks (CNNs) have been developed, the accuracy of the proposed models varies. Up-to-date, the issue regarding the convolutional neural network architecture with best performance for detecting concrete cracks is still debated in many previous studies. In this paper, we choose three established open-source CNN models (Model1, Model2, and Model3) which have been well-illustrated and verified in previous studies and test them for the purpose of crack detection of concrete structures. The chosen three models are trained using a concrete crack dataset containing 40,000 images those with 227 × 227-pixel in size. The performance of three different convolutional neural network (CNN) models was then evaluated. The comprehensive comparison result indicates that Model2 which used batch normalization is capable of the best performance amongst the three models as selected for concrete cracks detection, with recording the highest classification accuracy and low loss. In a conclusion, we recommend Model2 for a concrete crack detection task.

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Multiclass damage detection in concrete structures using a transfer learning‐based generative adversarial networks

Dunphy

Sadhu

Wang

2022

Structural Contr & Hlth

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A large amount of the world's existing infrastructure is reaching the end of its service life, requiring intervention in the form of structural rehabilitation or replacement. A critical aspect of such asset management is the condition assessment of these structures to evaluate their existing health and dictate the scheduling and extent of required rehabilitation. It has been demonstrated that human-based manual inspections face logistical constraints and are expensive, time extensive, and subjective, depending on the knowledge of the inspection. Recently, autonomous vision-based techniques have been proposed as an alternative, more accurate method for the inspection of deteriorating structures. Convolutional neural networks (CNNs) have demonstrated state-of-the-art accuracy with respect to damage classification for concrete structures and are often implemented to process images taken from vision-based sensors such as cameras, smartphones, and drones. However, these archetypes require a large database of annotated images to train the network to an accurate level, which is not readily available for real-life structures. Moreover, CNNs are limited to the extent by which they are trained; they are often only trained for binary damage classification of a singular material model. This paper addresses these challenges of CNNs through the application of a generative adversarial network (GANs) for multiclass damage detection of concrete structures. The proposed GAN is trained using the SDNET2018 dataset to detect cracking, spalling, pitting, and construction joints in concrete surfaces. Moreover, transfer learning is implemented to transfer the learned features of the GAN to a CNN architecture to allow for accurate image classification. It is concluded that, for a 0%-30% reduction in the amount of labeled data used, the proposed GAN method has comparable accuracy to traditional CNNs.

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Classification and quantification of cracks in concrete structures using deep learning image-based techniques

Cited by 121 publications

References 16 publications

Attention‐Based Convolutional Neural Network for Pavement Crack Detection

Attention‐Based Convolutional Neural Network for Pavement Crack Detection

Exploring the Detection Accuracy of Concrete Cracks Using Various CNN Models

Multiclass damage detection in concrete structures using a transfer learning‐based generative adversarial networks

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