Enhanced recognition of bonding interface defects within CFRP-steel adhesive structures via thermal signals in low-power vibrothermography

Carbon fiber reinforced polymers (CFRP) have been used as one of the options to strengthen steel structures through adhesive bonding, particularly in specific applications where traditional strengthening methods may not be suitable. Therefore, it becomes crucial to perform inspections on the resulti...

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Published in:Construction & building materials Vol. 456; p. 139329
Main Authors: Zhang, Yubin, Xu, Changhang, Liu, Pengqian, Xie, Jing, Liu, Rui, Zhao, Qing
Format: Journal Article
Language:English
Published: Elsevier Ltd 20.12.2024
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ISSN:0950-0618
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Abstract Carbon fiber reinforced polymers (CFRP) have been used as one of the options to strengthen steel structures through adhesive bonding, particularly in specific applications where traditional strengthening methods may not be suitable. Therefore, it becomes crucial to perform inspections on the resulting CFRP-steel adhesive structures (CSAS) to ensure their structural integrity and safety. However, the distinct physical properties of CFRP, epoxy resin, and steel pose significant challenges to accurately inspecting bonding interface defects of such special hybrid engineering structures. To address these challenges, a new approach, streamlined one-dimensional convolutional denoising autoencoder-low-power vibrothermography (SOCDAE-LVT), is proposed in this study to enhance the recognition of bonding interface defects within CSAS. This approach utilizes thermal signals from low-power vibrothermography (LVT) to enhance the recognizability of CSAS bonding interface defects. A low-power vibrothermography inspection system was developed to acquire thermal signals on the surface of CSAS samples. A streamlined one-dimensional convolutional denoising autoencoder (SOCDAE) model was designed for robust representation extraction of the thermal signal at each pixel point. The study further investigated the impact of different types of added noise and signal pre-processing approaches on the performance of the SOCDAE-LVT, aiming to optimize its effectiveness. By comparing qualitatively and quantitatively with the state-of-the-art approaches, the results show that the proposed approach can better improve the recognizability of defects. The enhanced recognizability of bonding interface defects enables accurate assessment of the quality of CSAS, thereby contributing to the safety of such structures. •A SOCDAE-LVT approach is proposed to enhance the recognition of bonding interface defects within CSAS.•Some factors affecting the performance of the SOCDAE-LVT approach are explored.•Experiments on CSAS samples containing bonding interface defects validated the effectiveness of the SOCDAE-LVT approach.•The SOCDAE-LVT approach outperforms some of the state-of-the-art approaches.
AbstractList Carbon fiber reinforced polymers (CFRP) have been used as one of the options to strengthen steel structures through adhesive bonding, particularly in specific applications where traditional strengthening methods may not be suitable. Therefore, it becomes crucial to perform inspections on the resulting CFRP-steel adhesive structures (CSAS) to ensure their structural integrity and safety. However, the distinct physical properties of CFRP, epoxy resin, and steel pose significant challenges to accurately inspecting bonding interface defects of such special hybrid engineering structures. To address these challenges, a new approach, streamlined one-dimensional convolutional denoising autoencoder-low-power vibrothermography (SOCDAE-LVT), is proposed in this study to enhance the recognition of bonding interface defects within CSAS. This approach utilizes thermal signals from low-power vibrothermography (LVT) to enhance the recognizability of CSAS bonding interface defects. A low-power vibrothermography inspection system was developed to acquire thermal signals on the surface of CSAS samples. A streamlined one-dimensional convolutional denoising autoencoder (SOCDAE) model was designed for robust representation extraction of the thermal signal at each pixel point. The study further investigated the impact of different types of added noise and signal pre-processing approaches on the performance of the SOCDAE-LVT, aiming to optimize its effectiveness. By comparing qualitatively and quantitatively with the state-of-the-art approaches, the results show that the proposed approach can better improve the recognizability of defects. The enhanced recognizability of bonding interface defects enables accurate assessment of the quality of CSAS, thereby contributing to the safety of such structures. •A SOCDAE-LVT approach is proposed to enhance the recognition of bonding interface defects within CSAS.•Some factors affecting the performance of the SOCDAE-LVT approach are explored.•Experiments on CSAS samples containing bonding interface defects validated the effectiveness of the SOCDAE-LVT approach.•The SOCDAE-LVT approach outperforms some of the state-of-the-art approaches.
ArticleNumber 139329
Author Zhang, Yubin
Liu, Rui
Liu, Pengqian
Xie, Jing
Zhao, Qing
Xu, Changhang
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  surname: Zhao
  fullname: Zhao, Qing
  organization: College of Mechanical and Electronic Engineering, China University of Petroleum (East China), Qingdao 266580, China
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Keywords Low-power vibrothermography
Streamlined one-dimensional convolutional denoising autoencoder
CFRP-steel adhesive structures
Bonding interface defects
Thermal signals
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Snippet Carbon fiber reinforced polymers (CFRP) have been used as one of the options to strengthen steel structures through adhesive bonding, particularly in specific...
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StartPage 139329
SubjectTerms Bonding interface defects
CFRP-steel adhesive structures
Low-power vibrothermography
Streamlined one-dimensional convolutional denoising autoencoder
Thermal signals
Title Enhanced recognition of bonding interface defects within CFRP-steel adhesive structures via thermal signals in low-power vibrothermography
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