Deep convolution neural network for damage identifications based on time-domain PZT impedance technique

Al-Azzawi, Osama; Wang, Dansheng

doi:10.1007/s12206-021-0401-y

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…Recently, CNN has been applied in the SHM tasks and achieved some recognized achievements. [43][44][45][46][47][48][49][50][51][52] Despite the abundant achievement that has been accomplished by CNN, it is generally challenging to fully train a deep model due to the gradient vanishing or exploding during back-propagation. Deep residual network (DRN) has arisen as a part of extremely deep architectures providing competitive precision and outstanding performances.…”

Section: In Vibration-based Shmmentioning

confidence: 99%

A novel structural damage identification method based on the acceleration responses under ambient vibration and an optimized deep residual algorithm

Al-Azzawi

Wang

2022

Structural Health Monitoring

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Recently, structural health monitoring (SHM) methods for civil structures have been investigated widely, especially Deep Learning (DL)-based methods. However, it is usually difficult to fully train a deep neural network, and thus, typical DL-based SHM methods are limited in terms of performance. While addressing these issues, in this paper, a novel methodology is proposed for smart damage identification of frame structures. The newly proposed SHM method is based on raw time-domain structural response signals and deep residual network (DRN). The introduced DRN algorithm has been designed and tested in an effective way for extracting and learning the optimum features of the 1D raw ambient vibration acceleration signals, without any need for engineered features. Also, the network’s performance has been optimized using Bayesian optimization, which clearly enhances the network’s accuracy and information flow across it. Next, the outputs of DRNs are further utilized through new methods for damage size estimation and damage localization. The proposed methodology has been evaluated using the datasets of numerical and experimental frames of the SHM benchmark problem and the dataset of a real-world full-scale truss bridge. The results show that the proposed method is capable of detecting, localizing, and quantifying structural damage accurately for all of the simulated cases of the two examples. Furthermore, conducted comparison studies have approved that the new approach is more efficient than other machine learning-based methods, and it can overcome the major limitations of Artificial intelligence-based SHM models.

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Section: In Vibration-based Shmmentioning

confidence: 99%

A novel structural damage identification method based on the acceleration responses under ambient vibration and an optimized deep residual algorithm

Al-Azzawi

Wang

2022

Structural Health Monitoring

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“…Although a small number of studies on damage detection have used some low-cost EMI sensing devices recently [42][43][44], they are still focused on frequency domain-based EMI methods and need to select the frequency ranges sensitive to damage. At present, there are few studies on direct measurement and utilization of impedance signals in time domain [45,46]. Furthermore, in most of the existing studies, limited items of generlizability and signal preprocessing tools are key to enable DL models to extract meaningful features from signals, which often adds computational cost.…”

Section: Introductionmentioning

confidence: 99%

Health monitoring of an assembly-type structure using raw time-domain EMI signals and a novel hybrid deep learning algorithm

Alazzawi,

Wang

2024

Smart Mater. Struct.

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Deep learning models are widely used to extract features from data through supervised or unsupervised methods. However, when these two methods are used separately, their generalization ability is insufficient. In this study, a SHM technology based on a novel hybrid deep learning (HDL) model and time-domain electromechanical impedance (TEMI) has been proposed. The HDL is a highly efficient combination of deep auto-encoder (DAE) and two-dimensional convolutional neural network (2-D CNN). The DAE is used for unsupervised reconstruction of the high-level features of 1-D TEMI signals, and then, the features extracted by DAE are arranged and fed into a supervised 2-D CNN that is designed to learn the optimal feature representations of the multi-label signals. This increases effectively the model's learnability and generalizability. The robustness of the developed approach has been tested using TEMI signals that are collected from a scaled model of a rectangular pipe gallery subjected to transversal joint damage. Results indicate that the maximal identification errors of joint damage severity are 2.4% and 9.33% when the corresponding structural conditions are used and unused by the training process of HDL, respectively.

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Stochastic dynamics of aircraft ground taxiing via improved physics-informed neural networks

Zhang,

Jin,

Wang

et al. 2024

Nonlinear Dyn

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Deep convolution neural network for damage identifications based on time-domain PZT impedance technique

Cited by 6 publications

References 34 publications

A novel structural damage identification method based on the acceleration responses under ambient vibration and an optimized deep residual algorithm

A novel structural damage identification method based on the acceleration responses under ambient vibration and an optimized deep residual algorithm

Health monitoring of an assembly-type structure using raw time-domain EMI signals and a novel hybrid deep learning algorithm

Stochastic dynamics of aircraft ground taxiing via improved physics-informed neural networks

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