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.

Review on Structural Health Evaluation with Acoustic Emission

“…Fatigue damage of bridges has always presented technical challenge and remote monitoring capability with AE has become a viable solution [149]. Well-known fatigue damage in recent years was at the San Francisco-Oakland Bay Bridge, which led to a large-scale AE monitoring of the affected bridge segments following a successful demonstration of AE's capability [150,151]. For this project, 640 sensors of 60-kHz resonance frequency were used to monitor 384 eyebars covering over 6-km distance.…”

Contribution of elastic wave approaches to civil engineering digital transformation

“…Acoustic emission is a very powerful method to know real-time evolution of crack assuming installation of AE sensors; however, the time span of deterioration or ageing of engineering concrete namely concrete structure takes a decade of years in minimum, meaning that there was no application of AE technique to install/ monitor the concrete structures for that long period, leading only a short time continuous monitoring in a level of laboratory by AE techniques. AE techniques shall be thus considered and applying only for unique cases such as experimental in-situ monitoring and safety confirmation monitoring when serious damages became obvious but shall be in-service until ultimate countermeasures [10]. Again in AE tomography, artificial excitations of elastic waves are not needed but source location algorithm of AE technique is applied to identify the naturally produced AE sources due to reversable motions of existing defects as fritting and friction among crack interfaces.…”

Structural Health Monitoring of Walking Dragline Excavator Using Acoustic Emission

“…The periodicity analysis allows to recognize the AE activity against the noise process. Some of the most complicated structures for AE structural health monitoring are railways and roads bridges [9,10]. The complicity consists of the fact that trains and cars are oftentimes a noise source and a load excitation, and the The most widely spread non-destructive testing (NDT) of dragline structural components is ultrasonic testing, which is carried out in the welded joints of an excavator twice a year.…”