The probability of acoustic emission (AE) detection associated with the fatigue crack extension in steel bridge component
seems to be an intractable problem because of the complexity of the AE sources. It is an ongoing challenge because the
AE sensors are not only sensitive to the AE signals but also the mechanical noises; and is very difficult to interpret the real
signals related to microcrack extension. Probability of detection may also be influenced by the medium of wave propagation,
threshold setting, sensitivity and frequency range of the sensors and the source–to–sensor distance. This paper presents the
probability of AE detection associated with fatigue crack extension in steel bridge element as a function of stress intensity
range. The real AE events associated with the fatigue crack extension are screened by using different filtering techniques.
For simplicity, the Poisson distribution is employed to calculate the probability of AE detection associated with fatigue crack
extension at different level of fatigue crack which may later facilitate to determine the priorities of instrumentation to the real
in-service bridges for structural health monitoring and hence reduces the cost of monitoring.
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