Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces

Le, Tien-Thinh; Nguyen, Van-Hai; Le, Minh Vuong

doi:https://doi.org/10.1155/2021/8858545

Applied Computational Intelligence and Soft Computing

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Abstract Introduction Materials and Methods Results and Discussion Conclusion Data Availability Conflicts of Interest References Copyright Related Articles

Research Article | Open Access

Volume 2021 | Article ID 8858545 | https://doi.org/10.1155/2021/8858545

Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces

Tien-Thinh Le,^1,2Van-Hai Nguyen,^1,2and Minh Vuong Le³

Academic Editor: Francesco Carlo Morabito

Received01 Oct 2020

Revised28 Jan 2021

Accepted19 Mar 2021

Published26 Mar 2021

Abstract

This paper is devoted to the development of a deep learning- (DL-) based model to detect crack fractures on concrete surfaces. The developed model for the classification of images was based on a DL Convolutional Neural Network (CNN). To train and validate the CNN model, a database containing 40,000 images of concrete surfaces (with and without cracks) was collected from the available literature. Several conditions on the concrete surfaces were taken into account such as illumination and surface finish (i.e., exposed, plastering, and paint). Various error measurement criteria such as accuracy, precision, recall, specificity, and F1-score were employed for accessing the quality of the developed model. Results showed that for the training dataset (50% of the database), the precision, recall, specificity, F1-score, and accuracy were 99.5%, 99.8%, 99.5%, 99.7%, and 99.7%, respectively. On the other hand, for the validating dataset, the precision, recall, specificity, F1-score, and accuracy are 96.5%, 98.8%, 96.6%, 97.7%, and 97.7%, respectively. Thus, the developed CNN model may be considered valid because it performs the classification of cracks well using the testing data. It is also confirmed that the developed DL-based model was robust and efficient, as it can take into account different conditions on the concrete surfaces. The CNN model developed in this study was compared with other works in the literature, showing that the CNN model could improve the accuracy of image classification, in comparison with previously published results. Finally, in further work, such model could be combined with Unmanned Aerial Vehicles (UAVs) to increase the productivity of concrete infrastructure inspection.

1. Introduction

Various infrastructures use concrete materials such as bridges, nuclear reactors, dams, and buildings. However, these construction facilities are affected by concrete damage after years of service [1]. Notably, one of the significant impacts that severely affected the durability of concrete and reinforced one is the presence of cracks [1]. Indeed, these cracks cause many problems for the reinforcement, such as corrosion and chemical attack [2]. Consequently, structural damage identification is shown to be inevitable to reduce the risks [3].

As the identification of cracks is crucial for the assessment of concrete damage, various techniques have been proposed for the maintenance of such infrastructures. Structural health monitoring mainly consists of using sensors to detect the changes in the stiffness of infrastructures as well as the initialization of corrosion [4–6]. However, such monitoring technique is commonly integrated into modern construction facilities. For existing infrastructures, especially concrete structures from the 1960s, this technique remains challenging [7]. Besides, the cost for the maintenance of substantial concrete infrastructure is currently expensive; for instance, an average budget of five billion euros per year is used in Europe, as mentioned by the European HEALCON project [8], for the maintenance and repair activities. Consequently, the development of more robust and efficient techniques is crucial, aiming at saving time and cost for the maintenance of substantial concrete infrastructures, especially for those presumably exceeding their expected service life [2].

One of the traditional methods that has been used for crack detection and propagation is the Finite Element Method (FEM). Many research works have been done in the literature regarding this problem. For example, in the work of Nahvi and Jabbari [9], the authors have combined experimental modal data and FEM to study the crack detection within a cantilever beam. In another work of Li et al. [10], the crack location inside structures has been studied using Wavelength Finite Element Methods (WFEMs). Other works on crack detection of beams and structures using FEM can be found in [11–16]. In addition, FEM can also be combined with a machine learning algorithm for crack detection. For example, in the work of He et al. [17], a genetic algorithm-based model optimized by FEM has been used for crack detection in a rotor-bearing model. The main difficulty of the crack detection problem using FEM is that the models are usually very complex and costly in terms of computational time. Indeed, using FEM to deal with cracks, even small ones require extremely refined mesh, which leads to problems with a high number of degrees of freedom.

Together with sensor equipment, many computer vision techniques have been proposed for the detection of cracks on the concrete surface [18–20]. These vision techniques were mainly developed based on deep learning (DL) algorithms for image processing, for instance, Convolutional Neural Networks (CNNs). Indeed, DL-based algorithms can provide many advantages to overcome the limitations of conventional image processing techniques [19], especially for crack detection [21]. As an example, Olivera and Correia [22] have developed an automatic crack detection based on the DL technique for assessing the damage in the Portuguese road system. In addition, Chen et al. [23] have improved the recognition of cracks in images using a CNN model. Besides, Nhat-Duc and Nguyen Quoc-Lam [24] have proposed a classification model using Support Vector Machine for the detection of cracks on asphalt pavement. The detection of cracks in bridge infrastructures has been successfully investigated by Xu et al. [25] using a CNN model. In another study, Nhat-Duc et al. [26] have proposed a hybrid CNN model based on the use of metaheuristic techniques for training the DL algorithm and application in crack recognition in the pavement surface. Obviously, DL-based techniques exhibit a significant ability to detect concrete crack damage robustly and reliably [27, 28]. Besides, a pretrained image-based recognition DL model could assist in the development of an automatic damage inspector, facilitating the detection of damage. In Gönenç-Sorguç [29], a comparison of several pretrained CNN models was investigated for the detection of cracks in building using AlexNet, ResNet, GoogleNet, and VGG. Indeed, such a pretrained DL model could be used for classifying quickly the images collected from vision capturing equipment. In several cases of large infrastructures such as bridge decks or high buildings, Unmanned Aerial Vehicles (UAVs) could be an appropriate choice as vision capturing equipment [30, 31]. As the development of UAV is highly increased recently, the combination of UAV and pretrained DL models could respond effectively and efficiently to the difficulty when maintaining large concrete infrastructures, saving time and cost [32, 33].

In order to overcome the difficulties of traditional approaches such as the Finite Element Method or other machine learning models that require complex input data and are costly in terms of computational time, the present study focuses on the development of an image-based CNN recognition model for the detection of cracks on concrete surfaces. To this aim, a database containing 40,000 images was served for the training and testing of the developed DL model. Various quality assessment criteria such as accuracy, precision, recall, specificity, and F1-score were employed for checking and validating the developed model. The structure of the present paper is organized as follows. The image database, as well as the research method, is presented in Section 2. The optimization of the image-based CNN model is described in Section 3, followed by the results related to the prediction capability of the proposed model. The final section concludes this study with several discussions. The developed model represents a high potential technique to be used as a concrete crack detection tool that can combine with an automatic workflow involving many types of efficient equipment such as UAV.

2. Materials and Methods

2.1. Database

In this work, a database of images with cracks was collected from the available literature [29, 34]. Derived from the walls and floors of several concrete buildings at the Middle East Technical University, the database contains two categories of the concrete surface, no cracks and with cracks. The distance between the concrete surface and the camera was approximately 1 m. Both the no crack and crack categories contain 20,000 images, and each image exhibits 227227 RGB pixels. Several samples of the database are shown in Figure 1. The images were captured on the same day with similar illumination. However, as various concrete surfaces were investigated (i.e., exposed, plastering, and paint) at different buildings, the variation in terms of surface finish and lighting conditions exists in these images. It should be noticed that this final database was generated from 458 high-resolution images (i.e., 40323024 pixels) as a data augmentation technique [35]. The dataset was randomly split into a training and validation dataset at a 50/50 ratio. Summary information of the database is indicated in Table 1.

2.2. Convolutional Neural Network (CNN)

CNN can be classified as a multilayer neural network whose main objective is to process two-dimensional input data, such as texts or images. As the definition of the neural network, CNN consists of multiple layers; each layer is composed of several neural nodes that have their own function. It is worth noting that the nodes in the same layer of the model are not interconnected. In this work, the CNN algorithm was selected for the development of an image-based DL model, inspiring by various success works of CNN for image classification in the literature. A method for image segmentation based on CNN was proposed by Arbelaez et al. [36]. In another study, a road detection system for self-driving cars was successfully developed by Teichmann et al. [37]. Last but not least, Camilo et al. [38] proposed a CNN-based mapping for solar photovoltaic using aerial imagery.

In terms of structure, the CNN model consists of 5 main layers as follows as depicted in Figure 2 [39–42]:(i)Input layer: this layer contains the image input data.(ii)Convolutional layer: the nodes in this layer work as filters whose main objective is to detect features in an image input using a convolution operator. This type of filter results in a map of activation called a feature map.(iii)Pooling layer: the main objective of this layer is to downsample the feature maps that are obtained from the convolutional layer. Technically, the results of the convolutional layer can be directly given to the classifier. However, this process can be very costly in terms of computational resources, especially with high-resolution image input data. The pooling layer provides an approach of downsampling the feature maps by summarizing the presence of features in patches. The results of the convolution layer are transferred to the pooling layer through a nonlinear activation function.(iv)Fully connected layer: the main objective of this layer is to take the output of the previous layer (i.e., the pooling layer) and then apply weights to predict the correct labels.(v)Output layer: this layer contains the prediction results of the problem.

As revealed in many studies, CNN exhibits several advantages compared to the conventional backpropagation neural network [40, 43]. CNN could also reduce the complexity of the model. More precisely, the weight parameters of CNN could be shared between neighborhood regions. Therefore, an acceleration in the training process could be obtained. For image application, this feature is vital because the neighborhood regions are usually carrying relevant information to the considered point [44, 45]. Besides, CNN exposes higher capability than a conventional neural network in feature extraction, especially for capturing local information (e.g., neighbor pixels in an image). Moreover, CNN might need fewer samples for the learning phase as well as a lower chance of overfitting than conventional neural networks. Finally, a completed description of CNN could be found in Nhat-Duc et al. [26] and Dorafshan et al. [46].

2.3. Quality Assessment Criteria

In this work, the error measurements of the classification task are designed in Figure 3, where(i)TP (i.e., true positive) explores the number of cracked images that are correctly identified as cracks(ii)TN (i.e., true negative) presents the number of no-cracked images that are correctly found as no cracks(iii)FP (i.e., false positive) shows the number of cracked images that are incorrectly classified as no cracks(iv)FN (i.e., false negative) exposes the number of no-cracked images that are incorrectly ranked as cracks

Based on these definitions, several quality assessment criteria could be computed, such as the following [25]:(i)Accuracy is defined as follows:(ii)Precision is defined as follows:(iii)Recall is defined as follows:(iv)Specificity is defined as follows:(v)F1-score is defined as follows:

3. Results and Discussion

3.1. Training of the CNN Model

In this work, a CNN model with 10 layers was trained using a Windows 10 Professional DELL T5610 Xeon E5-2680V2 40 Threads 128G RAM. Stochastic gradient descent with momentum was applied for training the DL neural network [47, 48]. Parameters during the training progress are indicated in Table 2. The model is evaluated every iteration using the validation dataset.

Table 3 details the proposed CNN’s architecture, including 10 layers such as input layer, convolutional layer, ReLU layer 1, fully connected layer 1, fully connected layer 2, batch normalization layer, ReLU layer 2, fully connected layer 3, softmax layer, and classification output layer. Sizes of activation, weights, and bias parameters are also indicated in Table 3 for each layer. It should be noticed that such an architecture was set based on the Deep Network Designer application [48].

Figures 4 and 5 show the training progress in terms of accuracy and loss, respectively. The corresponding values of accuracy and loss using the validation dataset are also highlighted. It is seen in Figures 4 and 5 that the training phase reaches a convergence after about 1200 iterations. Besides, good results of accuracy and loss were also obtained for the validation dataset.

3.2. Model Performance

In this section, the performance of the trained CNN model is presented. The capability of the model in detecting cracks is shown in Figure 6 for several samples. It is seen that the model can detect the cracks based on the contrast between the background and the cracks. Figures 7(a) and 7(b) show the confusion matrices of the training and testing data, respectively. Other quality assessment criteria are highlighted in Table 4. It is seen that, for the training dataset, the precision, recall, specificity, F1-score, and accuracy are 99.5%, 99.8%, 99.5%, 99.7%, and 99.7%, respectively. On the other hand, for the testing dataset, the precision, recall, specificity, F1-score, and accuracy are 96.5%, 98.8%, 96.6%, 97.7%, and 97.7%, respectively. Therefore, the CNN model may be considered valid because it performs the classification of cracks well using the validating data. It is also confirmed that the developed DL-based model is robust and efficient, as it can take into account different conditions on the concrete surface such as illumination, surface finish, and humidity.

(a)

(b)

(a)

(b)

3.3. Discussion

As indicated in the confusion matrix, there were several cases where the CNN model could not detect the cracks in the images. The false detection was tracked, and the corresponding images are shown in Figure 8, classified into three main categories, including images with cracks in the corners, images with low resolution, and images with too small cracks, respectively. In these cases, the CNN model could not perform the recognition task well because the contrast between the cracks and the background is poor [23]. In the first configuration, the cracks only occupy a small portion of the image. Consequently, the chance of the detection task is reduced. On the other hand, although all the images were captured on the same day for illumination condition purposes, however, as many buildings were investigated, the variation in the obtained images was inevitable. In addition, the detection for small cracks, especially for those at the pixel level, by using the image-based DL technique remains challenging [49].

(a)

(b)

(c)

Nonetheless, without solving complex equations, the CNN model was optimized for classifying the cracked images efficiently, saving time, and avoiding high computational costs. The performance of the developed CNN model was quantified based on various quality assessment criteria. A highlight of previous studies involving the reference, the training function, and the number of data, the size of images, the training/testing ratio, and the values of quality assessment criteria is given in Table 5. In terms of the value of quality assessment criteria, the proposed CNN model in this study improves the classification of cracked images, making it even more accurate than previously published results. However, different types of cracks were not considered yet in these works.

3.4. Future Work for Practical Application

As revealed in the introduction, a pretrained CNN model could assist in the development of an automatic damage inspector for concrete infrastructures [32]. A working process of the envisioned automatic system is shown in Figure 9. First, the images of concrete infrastructures (i.e., bridge, building, etc.) are collected as large datasets by drones, as this equipment could increase the productivity for image capturing. Second, all images are sent to the treatment center for processing and classification using the pretrained CNN model. Finally, the AI-assisted damage inspector gives an evaluation and feedback. As the developed CNN model could work with large datasets, it is expected that the algorithm could be helpful for experts in damage assessment by increasing yielding, saving time, and cost. However, it should be noticed that such a system should have the ability to be corrected by experts because human expertise is always crucial.

4. Conclusion and Outlook

This work was devoted to the development of a DL model for the classification of cracked and no-cracked images captured on concrete surfaces. A dataset containing 40,000 image samples of crack and noncrack labels was extracted from the available literature to train and validate the proposed model. The CNN model was trained for applying to 227 × 227-pixel images. The model achieved excellent classification performance, for the training dataset, the precision, recall, specificity, F1-score, and accuracy were 99.5%, 99.8%, 99.5%, 99.7%, and 99.7%, respectively, whereas, for the testing dataset, the precision, recall, specificity, F1-score, and accuracy were 96.5%, 98.8%, 96.6%, 97.7%, and 97.7%, respectively. As various concrete surfaces in different buildings were studied (i.e., exposed, plastering, and paint), thus the error measurements of the CNN model were in an accepted range.

However, in further research, different types of cracks should be classified (i.e., ranking by the thickness or density of cracks). Consequently, more classes will appear in the classification problem. Therefore, efficient training algorithms should be investigated, including metaheuristic techniques. Nonetheless, an efficient tool for the classification of cracks with different sizes may be useful for maintenance and repair procedures. Moreover, the detection of cracks at pixel level should be considered in further researches. Coupling between structural health monitoring and DL-based techniques should be further investigated for combining the feature of each method. Finally, other deep learning approaches can be further applied to improve the performance of the prediction problem. For example, in the work of Ieracitano et al. [50], the authors have used a model that is a combination of unsupervised learning autoencoder and supervised learning multilayer perceptron for defect detection of nanomaterials. The obtained results have been proven to be very promising, which outperformed other classical machine learning approaches. It is then interesting to apply such model to the crack detection problem. In another work of Shengqi et al. [51], a deep learning model using feature visualization and quality evaluation has been introduced for the defect recognition problem of the steel surface. This model can also be a good candidate for the crack detection problem for our future works.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

X. Xi and S. Yang, “Time to surface cracking and crack width of reinforced concrete structures under corrosion of multiple rebars,” Construction and Building Materials, vol. 155, pp. 114–125, 2017.
View at: Google Scholar
H. C. Phan, T.-T. Le, N. D. Bui et al., “An empirical model for bending capacity of defected pipe combined with axial load,” International Journal of Pressure Vessels and Piping, vol. 191, Article ID 104368, 2021.
View at: Publisher Site | Google Scholar
M. Zemann, N. Herrmann, and F. Dehn, “Calcite formation on steamed concrete surfaces and its potential for sealing cracks,” Construction and Building Materials, vol. 203, pp. 1–8, 2019.
View at: Publisher Site | Google Scholar
S. Alla and S. S. Asadi, “Integrated methodology of structural health monitoring for civil structures,” Materials Today: Proceedings, vol. 27, 2020.
View at: Publisher Site | Google Scholar
G. Fan, J. Li, and H. Hao, “Vibration signal denoising for structural health monitoring by residual convolutional neural networks,” Measurement, vol. 157, Article ID 107651, 2020.
View at: Publisher Site | Google Scholar
H. Thanh Duong, H. Chi Phan, T.-T. Le, and N. Duc Bui, “Optimization design of rectangular concrete-filled steel tube short columns with balancing composite motion optimization and data-driven model,” Structures, vol. 28, pp. 757–765, 2020.
View at: Publisher Site | Google Scholar
T.-T. Le, “Practical machine learning-based prediction model for axial capacity of square CFST columns,” Mechanics of Advanced Materials and Structures, vol. 10, pp. 1–16, 2020.
View at: Publisher Site | Google Scholar
D. V. Hemelrijck, S. Vanlanduit, A. Anastasopoulos, and T. Philippidis, “Emerging technologies in non-destructive testing VI,” in Proceedings of the 6th International Conference on Emerging Technologies in Non-destructive Testing, CRC Press, Brussels, Belgium, May 2015.
View at: Google Scholar
H. Nahvi and M. Jabbari, “Crack detection in beams using experimental modal data and finite element model,” International Journal of Mechanical Sciences, vol. 38, pp. 537–548, 2002.
View at: Google Scholar
B. Li, X. F. Chen, J. X. Ma, and Z. J. He, “Detection of crack location and size in structures using wavelet finite element methods,” Journal of Sound and Vibration, vol. 285, no. 4-5, pp. 767–782, 2005.
View at: Publisher Site | Google Scholar
G.-L. Qian, S.-N. Gu, and J.-S. Jiang, “The dynamic behaviour and crack detection of a beam with a crack,” Journal of Sound and Vibration, vol. 138, no. 2, pp. 233–243, 1990.
View at: Publisher Site | Google Scholar
T.-T. Le, “Probabilistic modeling of surface effects in nano-reinforced materials,” Computational Materials Science, vol. 186, Article ID 109987, 2021.
View at: Publisher Site | Google Scholar
H. B. Dong, X. F. Chen, B. Li, K. Y. Qi, and Z. J. He, “Rotor crack detection based on high-precision modal parameter identification method and wavelet finite element model,” Mechanical Systems and Signal Processing, vol. 23, no. 3, pp. 869–883, 2009.
View at: Publisher Site | Google Scholar
T.-T. Le, “Practical hybrid machine learning approach for estimation of ultimate load of elliptical concrete-filled steel tubular columns under axial loading,” Advances in Civil Engineering, vol. 2020, Article ID 8832522, 19 pages, 2020.
View at: Publisher Site | Google Scholar
T.-T. Le, “Probabilistic investigation of the effect of stochastic imperfect interfaces in nanocomposites,” Mechanics of Materials, vol. 151, Article ID 103608, 2020.
View at: Publisher Site | Google Scholar
T.-T. Le and H. C. Phan, “Prediction of ultimate load of rectangular CFST columns using interpretable machine learning method,” Advances in Civil Engineering, vol. 2020, Article ID 8855069, 16 pages, 2020.
View at: Publisher Site | Google Scholar
Y. He, D. Guo, and F. Chu, “Using genetic algorithms and finite element methods to detect shaft crack for rotor-bearing system,” Mathematics and Computers in Simulation, vol. 57, no. 1-2, pp. 95–108, 2001.
View at: Publisher Site | Google Scholar
N.-D. Hoang and Q.-L. Nguyen, “Automatic recognition of asphalt pavement cracks based on image processing and machine learning approaches: a comparative study on classifier performance,” Mathematical Problems in Engineering, vol. 2018, Article ID 6290498, 16 pages, 2018.
View at: Publisher Site | Google Scholar
D. Zhang, Q. Li, Y. Chen, M. Cao, L. He, and B. Zhang, “An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection,” Image and Vision Computing, vol. 57, pp. 130–146, 2017.
View at: Publisher Site | Google Scholar
N. X. Ho and T.-T. Le, “Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes,” Measurement, vol. 176, Article ID 109198, 2021.
View at: Publisher Site | Google Scholar
D. Rabinovich, D. Givoli, and S. Vigdergauz, “XFEM-based crack detection scheme using a genetic algorithm,” International Journal for Numerical Methods in Engineering, vol. 71, no. 9, pp. 1051–1080, 2007.
View at: Publisher Site | Google Scholar
H. Oliveira and P. L. Correia, “Automatic road crack detection and characterization,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 1, pp. 155–168, 2013.
View at: Publisher Site | Google Scholar
K. Chen, A. Yadav, A. Khan et al., “Improved crack detection and recognition based on convolutional neural network,” Modelling and Simulation in Engineering, vol. 2020, Article ID 8796743, 8 pages, 2019.
View at: Publisher Site | Google Scholar
H. Nhat-Duc and T. B. D. Nguyen Quoc-Lam, “Image processing–based classification of asphalt pavement cracks using support vector machine optimized by artificial bee colony,” Journal of Computing in Civil Engineering, vol. 32, Article ID 4018037, 2018.
View at: Publisher Site | Google Scholar
H. Xu, X. Su, Y. Wang, H. Cai, K. Cui, and X. Chen, “Automatic bridge crack detection using a convolutional neural network,” Applied Sciences, vol. 9, no. 14, p. 2867, 2019.
View at: Publisher Site | Google Scholar
H. Nhat-Duc, Q.-L. Nguyen, and V.-D. Tran, “Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network,” Automation in Construction, vol. 94, pp. 203–213, 2018.
View at: Publisher Site | Google Scholar
T. A. Carr, M. D. Jenkins, M. I. Iglesias et al., “Road crack detection using a single stage detector based deep neural network,” in Proceedings of the 2018 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS), pp. 1–5, Salerno, Italy, June 2018.
View at: Google Scholar
C. V. Dung and L. D. Anh, “Autonomous concrete crack detection using deep fully convolutional neural network,” Automation in Construction, vol. 99, pp. 52–58, 2019.
View at: Publisher Site | Google Scholar
A. Gönenç-Sorguç, “Ozgenel cf performance comparison of pretrained convolutional neural networks on crack detection in buildings,” in Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC 2018), Berlin, Germany, July 2018.
View at: Google Scholar
S. Dorafshan, R. J. Thomas, and M. Maguire, “Benchmarking image processing algorithms for unmanned aerial system-assisted crack detection in concrete structures,” Infrastructures, vol. 4, no. 2, p. 19, 2019.
View at: Publisher Site | Google Scholar
F. C. Pereira and C. E. Pereira, “Embedded image processing systems for automatic recognition of cracks using UAVs,” IFAC-PapersOnLine, vol. 48, no. 10, pp. 16–21, 2015.
View at: Publisher Site | Google Scholar
S. Sun and B. Wang, “Low-altitude UAV 3D modeling technology in the application of ancient buildings protection situation assessment,” Energy Procedia, vol. 153, pp. 320–324, 2018.
View at: Publisher Site | Google Scholar
T.-T. Le and M. V. Le, “Development of user-friendly kernel-based Gaussian process regression model for prediction of load-bearing capacity of square concrete-filled steel tubular members,” Materials and Structures, vol. 54, p. 59, 2021.
View at: Publisher Site | Google Scholar
ÇF. Özgenel, “Concrete crack images for classification,” 2018.
View at: Google Scholar
L. Zhang, F. Yang, Y. D. Zhang, and Y. J. Zhu, “Road crack detection using deep convolutional neural network,” in Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), pp. 3708–3712, Anchorage, Alaska, USA, September 2016.
View at: Google Scholar
P. Arbeláez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 898–916, 2011.
View at: Publisher Site | Google Scholar
M. Teichmann, M. Weber, M. Zöllner et al., “MultiNet: real-time joint semantic reasoning for autonomous driving,” in Proceedings of the 2018 IEEE Intelligent Vehicles Symposium, vol. IV, pp. 1013–1020, Changshu, Suzhou, China, June 2018.
View at: Google Scholar
J. Camilo, R. Wang, L. M. Collins et al., “Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery,” 2018.
View at: Google Scholar
G. Bayar and T. Bilir, “A novel study for the estimation of crack propagation in concrete using machine learning algorithms,” Construction and Building Materials, vol. 215, pp. 670–685, 2019.
View at: Publisher Site | Google Scholar
D.-X. Zhou, “Theory of deep convolutional neural networks: Downsampling,” Neural Networks, vol. 124, pp. 319–327, 2020.
View at: Publisher Site | Google Scholar
K. Khan, M. Attique, R. U. Khan, I. Syed, and T.-S. Chung, “A multi-task framework for facial attributes classification through end-to-end face parsing and deep convolutional neural networks,” Sensors, vol. 20, no. 2, p. 328, 2020.
View at: Publisher Site | Google Scholar
L. Zhang, J. Wu, Y. Fan, H. Gao, and Y. Shao, “An efficient building extraction method from high spatial resolution remote sensing images based on improved mask R-CNN,” Sensors, vol. 20, no. 5, p. 1465, 2020.
View at: Publisher Site | Google Scholar
T.-T. Le, “Prediction of tensile strength of polymer carbon nanotube composites using practical machine learning method,” Journal of Composite Materials, vol. 55, no. 6, 2020.
View at: Publisher Site | Google Scholar
J. Huyan, W. Li, S. Tighe, J. Zhai, Z. Xu, and Y. Chen, “Detection of sealed and unsealed cracks with complex backgrounds using deep convolutional neural network,” Automation in Construction, vol. 107, Article ID 102946, 2019.
View at: Publisher Site | Google Scholar
X. Lv, F. Duan, J.-J. Jiang, X. Fu, and L. Gan, “Deep active learning for surface defect detection,” Sensors, vol. 20, no. 6, p. 1650, 2020.
View at: Publisher Site | Google Scholar
S. Dorafshan, R. J. Thomas, and M. Maguire, “Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete,” Construction and Building Materials, vol. 186, pp. 1031–1045, 2018.
View at: Publisher Site | Google Scholar
N. Qian, “On the momentum term in gradient descent learning algorithms,” Neural Networks, vol. 12, no. 1, pp. 145–151, 1999.
View at: Publisher Site | Google Scholar
The MathWorks, MATLAB, SDC Publications, Natick, MA, USA, 2018.
A. Zhang, K. C. P. Wang, B. Li et al., “Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network,” Computer-Aided Civil and Infrastructure Engineering, vol. 32, no. 10, pp. 805–819, 2017.
View at: Publisher Site | Google Scholar
C. Ieracitano, A. Paviglianiti, M. Campolo et al., “A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 1, pp. 64–76, 2021.
View at: Google Scholar
G. Shengqi, M. Lei, and H. Lu, “A steel surface defect recognition algorithm based on improved deep learning network model using feature visualization and quality evaluation,” IEEE Access, vol. 8, 2020.
View at: Google Scholar

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Copyright © 2021 Tien-Thinh Le et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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