Structural health monitoring research under varying temperature condition: a review

Han, Qinghua; Ma, Qian; Xu, Jie; Liu, Ming

doi:10.1007/s13349-020-00444-x

Cited by 87 publications

(40 citation statements)

References 99 publications

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“…However, this type of contact detection method is susceptible to environmental factors such as temperature and humidity (Xia et al, 2012). Although some methods have been developed to compensate for these effects (Erazo et al, 2019;Han et al, 2020), detecting accurate damage parameters using contact sensors remains a challenge. Vision-based crack inspection method can overcome these shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Crack detection using fusion features‐based broad learning system and image processing

Zhang

Yuen

2021

Computer aided Civil Eng

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Deep learning has been widely applied to vision-based structural damage detection, but its computational demand is high. To avoid this computational burden, a novel crack detection system, namely, fusion features-based broad learning system (FF-BLS), is proposed for efficient training without GPU acceleration. In FF-BLS, a convolution module with fixed weights is used to extract the fusion features of images. Feature nodes and enhancement nodes randomly generated by fusion features are used to estimate the output of the network. Meanwhile, the proposed FF-BLS is a dynamical system, which achieves incremental learning by adding nodes. Thus, the trained FF-BLS model can be updated efficiently with additional data, and this substantially reduces the training cost. Finally, FF-BLS was applied to crack detection. Compared with some well-known deep convolutional neural networks (VGG16, ResNet50, InceptionV3, Xception, and Efficient-Net), the FF-BLS achieved a similar level of recognition accuracy, but the training speed was increased by more than 20 times.

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Section: Introductionmentioning

confidence: 99%

Crack detection using fusion features‐based broad learning system and image processing

Zhang

Yuen

2021

Computer aided Civil Eng

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“…SHM techniques have been developed for many years [1][2][3][4][5][6][7][8][9][10]. Generally, the vibration based SHM methods are the most widely adopted.…”

Section: Introductionmentioning

confidence: 99%

Review on Vibration-Based Structural Health Monitoring Techniques and Technical Codes

2021

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Structural damages occur in modern structures during operations due to environmental and human factors. The damages accumulating with time may lead to a significant decrease in structure performance or even destruction; natural symmetry is broken, resulting in an unexpected life and economic loss. Therefore, it is necessary to monitor the structural response to detect the damage in an early stage, evaluate the health condition of structures, and ensure the operation safety of structures. In fact, the structure and the evaluation can be considered as a special symmetry. Among several SHM methods, vibration-based SHM techniques have been widely adopted recently. Hence, this paper reviews the vibration-based SHM methods in terms of the vibrational parameters used. In addition, the technical codes on vibration based SHM system have also been reviewed, since they are more important in engineering applications. Several related ISO standards and national codes have been developed and implemented, while more specific technical codes are still required to provide more detailed guidelines in practice to maintain structure safety and natural symmetry.

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“…Temperature variations and uncertainties about loadings may lead to changes in dynamic parameters that can be mistakenly attributed to structural damage. In addition to the procedures used to mitigate the ambient effects [13][14][15], many contributions have been proposed to circumvent this problem, including different signal processing techniques for analyzing dynamic data based on metrics or parameters extracted from the signals in the time domain or any other transformed domain [16].…”

Section: Introductionmentioning

confidence: 99%

Numerical and Experimental Evaluation of Structural Changes Using Sparse Auto-Encoders and SVM Applied to Dynamic Responses

et al. 2021

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The present work evaluates the deep learning algorithm called Sparse Auto-Encoder (SAE) when applied to the characterization of structural anomalies. This study explores the SAE’s performance in a supervised damage detection approach to consolidate its application in the Structural Health Monitoring (SHM) field, especially when dealing with real-case structures. The main idea is to use the SAE to extract relevant features from the monitored signals and the well-known Support Vector Machine (SVM) to classify such characteristics within the context of an SHM problem. Vibration data from a numerical beam model and a highway viaduct in Brazil are considered to assess the proposed approach. In both analyzed examples, the efficiency of the implemented methodology achieved more than 99% of correct damage structural classifications, supporting the conclusion that SAE can extract relevant characteristics from dynamic signals that are useful for SHM applications.

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Structural health monitoring research under varying temperature condition: a review

Cited by 87 publications

References 99 publications

Crack detection using fusion features‐based broad learning system and image processing

Crack detection using fusion features‐based broad learning system and image processing

Review on Vibration-Based Structural Health Monitoring Techniques and Technical Codes

Numerical and Experimental Evaluation of Structural Changes Using Sparse Auto-Encoders and SVM Applied to Dynamic Responses

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