Abstract
Recently, Intelligence-based structural health monitoring (SHM) methods have investigated widely. Most of these methods are for detecting and classifying different structural damages by the means of features extraction from the structural responses signals, for instance different back propagation artificial neural networks SHM based methods. However, automatic features extraction, that eliminates the need for expertise and performing visual inspection to evaluate structures status is still a big challenge. In this study, therefore, a novel convolution neural network-based algorithm along with a hybrid training method has been proposed to detect, quantify and localize structural damage. The proposed method has been evaluated experimentally, many damaged and undamaged structural conditions have been conducted, acquiring samples of time-domain PZT impedance response signals from a beam. As the results show that, the method obtained a significant execution on damage detection, damage size evaluation and damage location recognition with high accuracy and reliability.
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Acknowledgments
The authors acknowledge the supports of the National Natural Science Fund of China (51278215) and Basic Research Program of China (contract number: 2016YFC0802002). The authors also gratefully acknowledge the work of the reviewers.
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Osama Alazzawi was born in Iraq, 1987. He is a Ph.D. student of the School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei. He received his Master degree in Structural Engineering from SHUAST U.P, India. His research interests include structural health monitoring, structural dynamic and vibration control.
Dansheng Wang is currently working as a Professor at School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology Wuahn, China. He received his Ph.D. degree in Structural Engineering from Huazhong University of Science and Technology, China. His research includes the field of structural health monitoring, damage identification, smart material and structures and finite element method.
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Alazzawi, O., Wang, D. Deep convolution neural network for damage identifications based on time-domain PZT impedance technique. J Mech Sci Technol 35, 1809–1819 (2021). https://doi.org/10.1007/s12206-021-0401-y
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DOI: https://doi.org/10.1007/s12206-021-0401-y