Abstract
The delayed fracture of high‐strength bolts occurs frequently in the bolt connections of long‐span steel bridges. This phenomenon can threaten the safety of structures and even lead to serious accidents in certain cases. However, the manual inspection commonly used in engineering to detect the fractured bolts is time‐consuming and inconvenient. Therefore, a computer vision‐based inspection approach is proposed in this paper to rapidly and automatically detect the fractured bolts. The proposed approach is realized by a convolutional neural network‐ (CNN‐) based deep learning algorithm, the third version of You Only Look Once (YOLOv3). A challenge for the detector training using YOLOv3 is that only limited amounts of images of the fractured bolts are available in practice. To address this challenge, five data augmentation methods are introduced to produce more labeled images, including brightness transformation, Gaussian blur, flipping, perspective transformation, and scaling. Six YOLOv3 neural networks are trained using six different augmented training sets, and then, the performance of each detector is tested on the same testing set to compare the effectiveness of different augmentation methods. The highest average precision (AP) of the trained detectors is 89.14% when the intersection over union (IOU) threshold is set to 0.5. The practicality and robustness of the proposed method are further demonstrated on images that were never used in the training and testing of the detector. The results demonstrate that the proposed method can quickly and automatically detect the delayed fracture of high‐strength bolts.
Highlights
High-strength bolt connections are widely used to assemble the load-bearing members of the steel structure in long-span steel bridges
The perspective transformation (PT) and FL both improve the average precision (AP) value on the testing set, and the highest increment of AP is induced by PT, achieving 4.52%, 13.39%, and 18.45% corresponding to three different intersection over union (IOU) thresholds
The promotion of the ability of the detector to detect vague images, brightness changes, and resolution changes cannot be reflected on the existing testing set, whereas the improvement of the ability to detect objects captured from different viewpoints is the most obvious, because all images were captured from different viewpoints
Summary
High-strength bolt connections are widely used to assemble the load-bearing members of the steel structure in long-span steel bridges. The popularity of the bolt connections is attributed to their low cost, high reliability, and rapid assembly [1]. These bridges are often operated in adverse environments and subject to corrosion [2, 3], vibration and fatigue [4, 5], and thermal cycling, which can contribute to the damage of bolts. The damage types of bolts that occur the most include the looseness and delayed fracture. The damage of bolts will threaten the safety of the bridges and may even lead to severe accidents. It is necessary to monitor the bolt condition in the daily operation and maintenance phase
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
