Date of Award


Document Type

Open Access Dissertation


Civil and Environmental Engineering


Civil engineering

First Advisor

Paul H Ziehl


The probability of Acoustic Emission (AE) detection associated with fatigue crack extension in steel bridge components is a difficult problem due to the complexity of the AE sources. AE is a very promising technique for structural health monitoring and automated micro-crack detection as it is generated by the material itself, unlike other nondestructive testing techniques (for example impact echo and ultrasonics), which require external input sources. Characterizing the source of AE is an ongoing challenge because AE sensors are not only sensitive to the AE signals but also to mechanical noise and reflections. It is therefore difficult to interpret the actual AE signals related to microcrack extension. Assessing the probability of detection is also influenced by the medium of wave propagation, threshold settings, sensitivity and frequency range of the sensors, and source-to-sensor distance. This dissertation addresses AE detection associated with fatigue crack extension in steel bridge elements and the associated probability of detection as a function of the stress intensity range. AE events associated with fatigue crack extension are assessed using moment tensor and b-value analysis. AE events are also synchronized with the strain field at the crack tip through the use of Digital Imaging Correlation (DIC). For simplicity, the Poisson and Weibull distributions are employed to calculate the probability of AE detection associated with fatigue crack extension at different levels of fatigue crack growth.