Graduate Thesis Or Dissertation

New acoustic emission applications in civil engineering

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  • Non-destructive testing methods and applications have become of increasing interest due to the worldwide aging and deteriorating infrastructure network. In the field of Civil Engineering, bridges and bridge components as well as non-structural elements such as roadway pavements for example, are affected. In particular, the Acoustic Emission (AE) technique offers the unique opportunity to monitor infrastructure components in real-time and detect sudden changes in the integrity of the monitored element. The principle is that dynamic input sources cause a stress wave to form, travel through the body, and create a transient surface displacement that can be recorded by piezo-electric sensors located on the surface. Commonly, analysis methods of purely qualitative nature are used to estimate the current condition or make predictions on the future state of a monitored component. Using quantitative analysis methods, source locations and characteristics can be deduced, similarly to the case for earthquake sources. If properly configured, crack formation and propagation can hence be quantified with this technique. The research work presented in this dissertation, however, goes past commonly found AE applications. Three novel applications have been developed: (1) a new prospective detection tool was discovered in the field of Transportation Engineering where AE sensors were employed to identify vehicles equipped with studded tires passing over bridges. Such vehicles cause costly damage each year to the roadway infrastructure network and this tool would enable gathering statistical data or enforcing legal dates for the use of studded tires. Load conditions of two full-scale laboratory bridge girders were estimated analyzing AE data collected from applied service-level loads using an earthquake prediction method called b-value analysis (2). It was found that this analysis method may assist in estimating the operating load conditions of in-service bridges. Finally (3), a novel framework for the development of a probabilistic stress wave source location algorithm based on Bayesian analysis methods is proposed. Markov Chain Monte Carlo simulation was employed to estimate model parameters and predict source locations in full probabilistic form. Because variability in the materials and errors in the model can be included, a more accurate solution is available.
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