Fatigue crack monitoring of steel bridge with coating sensor based on potential difference method

Fatigue cracks are common in steel bridges. For the real-time monitoring of fatigue cracks during the service of steel bridges, this study proposes a coating sensor for fatigue crack monitoring of steel bridges based on the potential difference method. First, the coating sensor was designed with three components including driving, sensing, and protective layers, and the characteristics of each part were measured. The equation for the spatial potential distribution of the coating sensor was derived and subsequently solved using a numerical simulation method. A theoretical model for crack growth prediction was proposed, and the parameters of the theoretical model were fitted using regression analysis associated with the simulated results. Six samples with artificial cracks were used to validate the accuracy of the theoretical model for crack-growth monitoring. Finally, the accuracy and applicability of the coating sensor for monitoring fatigue crack growth were verified using fatigue tests. The results indicate that the coating sensor can easily crack as fatigue cracks grow in steel plates. The predicted and actual crack lengths in both the artificial crack growth tests and real fatigue crack growth experiments demonstrate good consistency. Moreover, comparing crack monitoring by coated sensors with image recognition crack monitoring using high-definition cameras shows that the fatigue crack monitoring of them matches well, demonstrating the accuracy and applicability of the coating sensor and corresponding theoretical model for fatigue crack monitoring.