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Correction published on 5 August 2021, see Sensors 2021, 21(16), 5280.
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Article

Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model

by
Hai Chien Pham
1,
Quoc-Bao Ta
2,
Jeong-Tae Kim
2,
Duc-Duy Ho
3,4,
Xuan-Linh Tran
5,6 and
Thanh-Canh Huynh
5,6,*
1
Applied Computational Civil and Structural Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
2
Ocean Engineering Department, Pukyong National University, Busan 48513, Korea
3
Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City 700000, Vietnam
4
Vietnam National University, Ho Chi Minh City 700000, Vietnam
5
Faculty of Civil Engineering, Duy Tan University, Danang 550000, Vietnam
6
Center for Construction, Mechanics and Materials, Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(12), 3382; https://doi.org/10.3390/s20123382
Submission received: 13 May 2020 / Revised: 10 June 2020 / Accepted: 11 June 2020 / Published: 15 June 2020
(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Seismic Protection)
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Abstract

In this study, we investigate a novel idea of using synthetic images of bolts which are generated from a graphical model to train a deep learning model for loosened bolt detection. Firstly, a framework for bolt-loosening detection using image-based deep learning and computer graphics is proposed. Next, the feasibility of the proposed framework is demonstrated through the bolt-loosening monitoring of a lab-scaled bolted joint model. For practicality, the proposed idea is evaluated on the real-scale bolted connections of a historical truss bridge in Danang, Vietnam. The results show that the deep learning model trained by the synthesized images can achieve accurate bolt recognitions and looseness detections. The proposed methodology could help to reduce the time and cost associated with the collection of high-quality training data and further accelerate the applicability of vision-based deep learning models trained on synthetic data in practice.
Keywords: structural health monitoring; damage detection; bolted connection; loosened bolts; bolt loosening; looseness detection; deep learning; R-CNN; image processing; Hough transform structural health monitoring; damage detection; bolted connection; loosened bolts; bolt loosening; looseness detection; deep learning; R-CNN; image processing; Hough transform

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MDPI and ACS Style

Pham, H.C.; Ta, Q.-B.; Kim, J.-T.; Ho, D.-D.; Tran, X.-L.; Huynh, T.-C. Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model. Sensors 2020, 20, 3382. https://doi.org/10.3390/s20123382

AMA Style

Pham HC, Ta Q-B, Kim J-T, Ho D-D, Tran X-L, Huynh T-C. Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model. Sensors. 2020; 20(12):3382. https://doi.org/10.3390/s20123382

Chicago/Turabian Style

Pham, Hai Chien, Quoc-Bao Ta, Jeong-Tae Kim, Duc-Duy Ho, Xuan-Linh Tran, and Thanh-Canh Huynh. 2020. "Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model" Sensors 20, no. 12: 3382. https://doi.org/10.3390/s20123382

APA Style

Pham, H. C., Ta, Q.-B., Kim, J.-T., Ho, D.-D., Tran, X.-L., & Huynh, T.-C. (2020). Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model. Sensors, 20(12), 3382. https://doi.org/10.3390/s20123382

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