Acoustic Emission Method for Real-Time Detection of Steel Fatigue Crack in Eyebar

Abstract

After the discovery of a significant crack in an eyebar (fracture-critical element) on the San Francisco–Oakland Bay Bridge in California, the California Department of Transportation (Caltrans) explored possible remote monitor solutions to provide the greatest possible safeguards for the some 200,000 vehicles that used the bridge daily. Caltrans selected an acoustic emission (AE) monitoring system that allowed the detection and localization of crack initiation and growth in real time. The AE method relies on the propagation of elastic waves released by a sudden stress–strain change at the crack tip. The challenge of the AE method in a field application is the disturbance of the data by extraneous noise sources. A robust damage detection algorithm is required to differentiate relevant damage data from secondary noise sources, such as friction. Caltrans required that the proposed monitoring solution be validated through laboratory testing. This paper presents the full-scale laboratory testing of two eyebars loaded under constant amplitude fatigue to detect and locate crack growth under intense friction-type noise sources with use of the AE method. Verification is presented of AE results with ultrasonics testing, dye penetrant, and visual methods to detect damage. Linear location accuracy with simulated signal sources is discussed. The pattern recognition algorithm to differentiate noise signals and crack growth signals is presented. On the basis of this study, the AE method will be used to continuously monitor 384 eyebars with 640 AE sensors on the San Francisco–Oakland Bay Bridge.