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

• Main Authors: Hai Chien Pham, Quoc-Bao Ta, Jeong-Tae Kim, Duc-Duy Ho, Xuan-Linh Tran, Thanh-Canh Huynh
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Sensors
• Subjects: structural health monitoring; damage detection; bolted connection; loosened bolts; bolt loosening; looseness detection
• Online Access: https://www.mdpi.com/1424-8220/20/12/3382

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.