Evaluation of thermal cracks on fire exposed concrete structures using Ripplet​ transform

Andrushia, A. Diana; Anand, N.; Arulraj, G. Prince

doi:10.1016/j.matcom.2020.07.024

Cited by 35 publications

(13 citation statements)

References 44 publications

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“…The study reported that model developed with the techniques gave accurate predictions, and also agrees perfectly with the experimental data. Andrushia et al [16] explored the use of ripplet transform for evaluating the thermal cracks on fire exposed concrete structures. Their study showed that crack detection capacity of the propped method and that of other transform domain-based crack detection methods and edge detection methods were similar, and with their model giving an accuracy limit of 95.01%.…”

Section: Buffmentioning

confidence: 99%

Modeling Temperature of Fire-Damaged Reinforced Concrete Buildings Based on Nondestructive Testing and Gene Algorithm Techniques

Awoyera

Olalusi

2021

Fire Technol

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It is a daunting task to ascertain the level of deterioration in structural concrete and embedded reinforcing steel after fire damage. Thus, this study focuses on modelling the temperature of fire-damaged reinforced concrete buildings based on nondestructive and Gene algorithm techniques. The study employed nondestructive data (rebound hammer number (RH) and ultrasonic pulse velocity (UPV)) from laboratory simulation tests on concrete elements and field forensic investigation of firedamaged buildings to develop mathematical models. Other data used for the model was the thickness of the member. The input parameters for the model were: RH, UPV, and breadth of the member, while the temperature was the output data. The study proposed a model based on genetic expression programming (GEP), with a correlation coefficient of 0.9895. The developed model generalised the data correctly with a high degree of accuracy; it would be a useful tool for the field assessment of fire-damaged reinforced concrete buildings.

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Section: Buffmentioning

confidence: 99%

Modeling Temperature of Fire-Damaged Reinforced Concrete Buildings Based on Nondestructive Testing and Gene Algorithm Techniques

Awoyera

Olalusi

2021

Fire Technol

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“…Song Ee Park [22] used deep learning technology and structured light technology composed of vision and two laser sensors to detect and quantify cracks on the surface of concrete structures. Diana Andrushia [23] proposed a method to detect thermal cracks using ripple transformation, and the main components were noise removal, image enhancement, crack detection and crack geometric parameter detection. Zheng [24] established a fully convolutional network crack detection model, which provided strong theoretical support and practical value for the detection and research of concrete surface cracks.…”

Section: Introductionmentioning

confidence: 99%

Complex Texture Contour Feature Extraction of Cracks in Timber Structures of Ancient Architecture Based on YOLO Algorithm

Yan

Liu

et al. 2022

Advances in Civil Engineering

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Deep learning has achieved good results in the crack detection of roads and bridges. However, the timber structures of ancient architecture have strong orthotropic anisotropy and complex microscopic structures, and the law of cracks development is extremely complex. The image data has a large proportion of pixels, which is obviously different from the background gray value, and there is timber grain noise, thus the existing methods cannot accurately extract the complex texture contour feature of cracks. In previous studies, we have verified that YOLO v5s is effective in crack detection in timber structures of ancient architecture. However, there are many different versions of YOLO series models. In order to find a better algorithm, this paper mainly adopts three models including YOLO v3, YOLO v4s-mish, and YOLO v5s to detect cracks in the timber structures of ancient architecture, and compares and analyzes the advantages and disadvantages of the three models. In the comparing process, we mainly have discussed the index performance of the three models in terms of training time, loss function, recall rate, and mAP value. We have summarized and analyzed the advantages and disadvantages of the three models in cracks detection of the timber structures of ancient architecture, and concluded the comparing results of the three models in cracks detection based on experiments. We published the first picture data set of cracks in timber structures of ancient architecture, and applied YOLO model in the intelligent identification field of cracks in timber structures of ancient architecture for the first time, which opened up a new idea for the intelligent operation and maintenance of the timber structures of ancient architecture.

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“…These methods are also based on edge-based features, 18 minimum spanning trees, Gabor filters, 19 wavelet features, and discrete Ripplet transform. 20 Indeed, these image processing methods have the potential in boosting efficiency and reliability in the field of structural health monitoring. However, due to the anisotropy of crack shapes and the complex surrounding environment of pavements, these methods cannot detect cracks accurately.…”

Section: Introductionmentioning

confidence: 99%

“…These traditional methods rely on various hand-made features and contrivable gradient features of each image pixel, then a binary classifier is employed to determine whether the image pixel contains cracks. These methods are also based on edge-based features, 18 minimum spanning trees, Gabor filters, 19 wavelet features, and discrete Ripplet transform 20 . Indeed, these image processing methods have the potential in boosting efficiency and reliability in the field of structural health monitoring.…”

Section: Introductionmentioning

confidence: 99%

Method of convolutional neural network with hybrid attention for crack detection

Chen

2022

J. Electron. Imag.

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. In recent years, automatic image crack detection has become a critical task for ensuring the safety of various facilities. Many researchers utilized convolutional neural networks (CNNs) for crack detection. However, the existing CNN methods have the limitation of the receptive fields and struggle to establish long dependencies and global background information. Aiming to address this problem, we propose a CNN method with an attention module and recurrent mechanism. In terms of the backbone network, we propose a backbone network named Attention-VGG-19. In our network, we remove the last three fully connected layers of VGG-19 to expect the meaningful side output with different scales and to reduce the number of parameters. The fully connected layers are computationally intensive. The proposed network also provides integrated direct supervision for features of each convolutional stage. Then, to promote important features and suppress unimportant features, an attention module called CrackP attention is proposed. In the recurrent mechanism, the module can be unrolled into R loops to connect the path between one pixel and its neighboring ones. To prove the effectiveness of our proposed method, we evaluate it on four public crack datasets, DeepCrack, crack forest dataset (CFD), Crack500, and CrackTree260, on which it achieves F-score (F1) values of 0.876, 0.652, 0.692, and 0.593, respectively. After a guided filter is added, our method achieves F1 values of 0.878, 0.638, 0.681, and 0.526 on these four datasets.

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Evaluation of thermal cracks on fire exposed concrete structures using Ripplet transform

Cited by 35 publications

References 44 publications

Modeling Temperature of Fire-Damaged Reinforced Concrete Buildings Based on Nondestructive Testing and Gene Algorithm Techniques

Modeling Temperature of Fire-Damaged Reinforced Concrete Buildings Based on Nondestructive Testing and Gene Algorithm Techniques

Complex Texture Contour Feature Extraction of Cracks in Timber Structures of Ancient Architecture Based on YOLO Algorithm

Method of convolutional neural network with hybrid attention for crack detection

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Evaluation of thermal cracks on fire exposed concrete structures using Ripplet​ transform

Cited by 35 publications

References 44 publications

Modeling Temperature of Fire-Damaged Reinforced Concrete Buildings Based on Nondestructive Testing and Gene Algorithm Techniques

Modeling Temperature of Fire-Damaged Reinforced Concrete Buildings Based on Nondestructive Testing and Gene Algorithm Techniques

Complex Texture Contour Feature Extraction of Cracks in Timber Structures of Ancient Architecture Based on YOLO Algorithm

Method of convolutional neural network with hybrid attention for crack detection

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Evaluation of thermal cracks on fire exposed concrete structures using Ripplet transform