Automation and efficiency of stress amplitude ratio in fatigue crack detection in steel structures

Abstract

Fatigue cracks in bridges are becoming more and more apparent every year and are a problem for many bridges. Fatigue cracks are a failure phenomenon that progresses gradually due to repeated stress and deformation and can have a significant impact on the safety of the structure. Therefore, it is important to detect fatigue cracks at an early stage and to perform appropriate repair and reinforcement. In this paper, experimental data from vibration fatigue tests conducted in the past is reused to investigate the possibility of simplifying the process of crack detection by automating stress calculation and graphing for the numerical processing of a large amount of data. We also plan to compile a database of this data and develop it into a machine-learning system for predicting crack propagation by studying trends in stress states during crack initiation and propagation.