Structural damage detection using convolutional neural networks combining strain energy and dynamic response

Chen, Gongfa; Gong, Panpan; Liu, Gen; Cui, Fangsen

doi:10.1007/s11012-019-01052-w

Cited by 44 publications

(23 citation statements)

References 23 publications

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“…Therefore, the selection of the feature extractor model would be an interesting focus for object detection. It should be noted that the detailed feature extractor contains the convolution, pooling, and ReLU layers [18]. Furthermore, anchor boxes (Figure 2) were adopted to detect classes of objects in the image.…”

Section: Yolo_v2mentioning

confidence: 99%

“…Meanwhile, a DCNN uses partial connections and pooling of neurons, thus, requires less computation, has better robustness, which makes the DCNN an effective and fast SHM method. A lot of research has been conducted for SHM based on the DCNN [16,17] by using vibration signals [12,18,19] and defect images [16,20,21]. In the field of defect imagesbased SHM [22], image classification is a popular method for automatic defect detection.…”

Section: Introductionmentioning

confidence: 99%

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Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

Liu

Chen

2021

Applied Sciences

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This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for ‘resnet18’, ‘alexnet’, and ‘vgg16’, respectively meanwhile, the ‘googlenet’ (AP = 0.84) and ‘mobilenetv2’ (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the ‘alexnet’ takes the least computational time, the ‘squeezenet’ and ‘resnet18’ are ranked second and third respectively; therefore, the ‘resnet18’ is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role.

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Section: Yolo_v2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

Liu

Chen

2021

Applied Sciences

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“…Convolutional networks are used with great success in numerous practical applications and have thus become the standard for recognition systems and image or video processing [ 27 ]. In recent years, CNNs have also been readily used in SHM systems, mainly for vibration analysis [ 14 , 28 , 29 , 30 ].…”

Section: Formulation Of the Problemmentioning

confidence: 99%

“…Unlike classical neural networks (currently often referred to as shallow networks), a CNN is built from neurons ordered in three directions: width, height, and depth, which allows for feature detection in the image as well as in time series [ 24 , 25 ]. CNN processing capabilities have been used repeatedly in applications related to computational mechanics [ 26 ], vibration analysis [ 27 ], and SHM [ 28 , 29 , 30 ]. In [ 31 ], a new network (named TICNN) was proposed for feature extraction and classification of models with external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks

Miller

Ziemiański

2021

Materials

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The aim of the following paper is to discuss a newly developed approach for the identification of vibration mode shapes of multilayer composite structures. To overcome the limitations of the approaches based on image analysis (two-dimensional structures, high spatial resolution of mode shapes description), convolutional neural networks (CNNs) are applied to create a three-dimensional mode shapes identification algorithm with a significantly reduced number of mode shape vector coordinates. The CNN-based procedure is accurate, effective, and robust to noisy input data. The appearance of local damage is not an obstacle. The change of the material and the occurrence of local material degradation do not affect the accuracy of the method. Moreover, the application of the proposed identification method allows identifying the material degradation occurrence.

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“…A Convolutional Neural Network (CNN) has been proposed by Teng et al [19] for damage detection of steel truss structures using two data sets: the modal strain energy or the combination of modal strain energy and dynamic response (acceleration). Damage in an element is described by its Young's modulus reduction.…”

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confidence: 99%

Editorial

Trovalusci

Cui

2020

Meccanica

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Structural damage detection using convolutional neural networks combining strain energy and dynamic response

Cited by 44 publications

References 23 publications

Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks

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