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Acoustic emission (AE) is a well-established nondestructive testing method for assessing the condition of liquid-filled tanks. Often the tank can be tested without the need for accurate location of AE sources. But sometimes, accurate location is required, such as in the case of follow-up inspections after AE has indicated a significant defect. Traditional computed location techniques that considered only the wave traveling through the shell of the tank have not proved reliable when applied to liquid-filled tanks. This because AE sensors are often responding to liquid-borne waves, that are not considered in the traditional algorithms. This paper describes an approach for locating AE sources on the wall of liquid filled tanks that includes two novel aspects: (i) the use of liquid-borne waves, and (ii) the use of a probabilistic algorithm. The proposed algorithm is developed within a Bayesian framework that considers uncertainties in the wave velocities and the time of arrival. A Markov Chain Monte Carlo is used to estimate the distribution of the AE source location. This approach was applied on a 102 inch diameter (29 000 gal) railroad tank car by estimating the source locations from pencil lead break with waveforms recorded. Results show that the proposed Bayesian approach for source location can be used to calculate the most probable region of the tank wall where the AE source is located.

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Developing numerical models of existing structural systems is challenging because of the uncertainty inherent on the development of the numerical model and the estimation of the structural parameters. This uncertainty is a combination of lack of knowledge (epistemic uncertainty) and inherent randomness on the system. This paper introduces a Model Updating Cognitive Systems (MUCogS) as a new paradigm for model updating of structural systems with incomplete data. MUCogS seeks to merge the computational power of computers with the analytical power of the analyst. In most cases, the posterior probability density function (PDF) within a Bayesian framework has one region of high probability. However, several regions of high probability can be obtained on the likelihood when data is incomplete. These areas can be considered by the analyst to enhance his/her knowledge about the structure. This paper discusses a methodology used to identify these regions of high probability without the need of calculating the complete likelihood using Modeling to Generate Alternatives (MGA).

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