Deep learning-based planar crack damage evaluation using convolutional neural networks

Long, Xiangyun; Zhao, Shulun; Jiang, Chen; Li, W.P.; Liu, C.H.

doi:10.1016/j.engfracmech.2021.107604

Cited by 29 publications

(6 citation statements)

References 24 publications

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“…Engineers and researchers are able to employ machine learning to improve the accuracy and efficiency of fracture analysis by integrating ANN with FEA [28][29][30]. ANN can efficiently learn from massive amounts of data generated by FEA simulations, capturing complicated interactions between input parameters including material properties, crack geometry, loading and boundary conditions, and meshing types and the accompanying fracture responses [31,32]. This allows for more precise modeling of key crack propagation behavior, stress intensity factor, and stress distributions around the crack tip.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Application of Machine Learning in Fracture Analysis of Edge Crack Semi-Infinite Elastic Plate

Hossein Moghtaderi,

Jedi,

Ariffin

et al. 2024

Frattura Ed Integrità Strutturale

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This paper discusses the application of machine learning techniques, notably artificial neural networks (ANN), in the fracture analysis of semi-infinite elastic plates with edge cracks. The Stress Intensity Factor (SIF) model for a semi-infinite plate with a tip crack is employed in the study, and Finite Element Analysis (FEA) is performed via ABAQUS CAE to build a comprehensive dataset containing numerical simulations data. To improve accuracy and reliability, data preprocessing is implemented, and ANN as a valuable machine learning model is trained with various variables describing crack propagation, stress distribution, and plate structure as input parameters. The suggested method is compared to established fracture analysis methods, proving its accuracy in predicting crack behavior and stress distribution under a variety of loading circumstances. The model provides useful insights into the behavior of edge cracks in semi-infinite elastic plates, enhancing material engineering and structural mechanics. The study demonstrates the potential of combining FEA and machine learning to improve fracture analysis capabilities, and it discusses limitations and future research directions, encouraging the exploration of advanced machine learning techniques and broader fracture scenarios for future fracture mechanics innovation.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Application of Machine Learning in Fracture Analysis of Edge Crack Semi-Infinite Elastic Plate

Hossein Moghtaderi,

Jedi,

Ariffin

et al. 2024

Frattura Ed Integrità Strutturale

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“…where A and B represent two overlapping detection boxes: As is clear from (7), the NMS algorithm zeroes with the detection box that is adjacent to M and greater than the threshold. If an object under detection appears in the overlapping region, the NMS algorithm will fail to detect the object, thereby reducing the accuracy of the detection model.…”

Section: Nonmaximum Suppressionmentioning

confidence: 99%

“…According to Cha et al [6], computer vision-based techniques were developed to overcome the limitations of visual inspection by trained human resources and to detect structural damage in images remotely. In 2021, Long et al [7] presented a novel deep learning based damage evaluation approach by using speckled images. A deep convolutional neural network (DCNN) for predicting the stress intensity factor (SIF) at the crack tip is designed.…”

Section: Introductionmentioning

confidence: 99%

Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

Feng

Tao

Lee

2021

Mathematical Problems in Engineering

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In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

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“…Rebar corrosion may be triggered if the cracks extend to the level of the rebars. The safety and use of the concrete structure can be compromised by disintegration and spalling of the concrete that might result from the development of corrosion or fire [6][7][8]. Concrete structure inspection for crack detection is essential for detecting damage and assessing conditions [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Generalizing infrastructure inspection: step transfer learning aided extreme learning machine for automated crack detection in concrete structures

Sohaib,

Hasan,

Chen

et al. 2024

Meas. Sci. Technol.

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Identification of damage and selection of a restoration strategy in concrete structures is contingent upon automatic inspection for crack detection and assessment. Most research on deep learning models for autonomous inspection has focused solely on measuring crack dimensions, omitting the generalization power of a model. This research utilizes a novel step transfer learning aided extreme learning machine (STELM) approach to develop an automatic assessment strategy for surface cracks in concrete structures. Step transfer learning (STL) is helpful in mining generalized abstract features from different sets of source images, and extreme learning machine (ELM) helps the proposed model overcome the optimization limitations of traditional artificial neural networks. The proposed model achieved at least 2.5%, 4.8%, and 0.8% improvement in accuracy, recall, and precision, respectively, in comparison to the other studies, indicating that the proposed model could aid in the automated inspection of concrete structures, ensuring high generalization ability.

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Deep learning-based planar crack damage evaluation using convolutional neural networks

Cited by 29 publications

References 24 publications

Application of Machine Learning in Fracture Analysis of Edge Crack Semi-Infinite Elastic Plate

Application of Machine Learning in Fracture Analysis of Edge Crack Semi-Infinite Elastic Plate

Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

Generalizing infrastructure inspection: step transfer learning aided extreme learning machine for automated crack detection in concrete structures

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