Bolt Loosening and Preload Loss Detection Technology Based on Machine Vision - Zhiqiang Shang; Xi Qin; Zejun Zhang; Hongtao Jiang (Buildings)
Steel bridges often experience bolt loosening and even fatigue fracture due to fatigue load, forced vibration, and other factors during operation, affecting structural safety. This study proposes a high-precision bolt key point positioning and recognition method based on deep learning to address the high cost, low efficiency, and poor safety of current bolt loosening identification methods. Additionally, a bolt loosening angle recognition method is proposed based on digital image processing technology. Using image recognition data, the angle-preload curve is revised. The established correlation between loosening angle and pretension for commonly utilized high-strength bolts provides a benchmark for identifying loosening angles. This finding lays a theoretical foundation for defining effective detection intervals in future bolt loosening recognition systems. Consequently, it enhances the system’s ability to deliver timely warnings, facilitating swift manual inspections and repairs.