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CSIC Research Talk
Friday 14th June 2020

'Bayesian intelligent structural health monitoring'
Dr Sin-Chi Kuok

Abstract:
Bayesian intelligent structural health monitoring (BISHM) integrates Bayesian inference and machine learning to develop an effective and efficient framework for reliable structural health monitoring (SHM). Bayesian inference provides a rigorous scheme for utilizing the available information to determine the optimal estimates and quantify the associated estimation uncertainty in the form of probability distribution. On the other hand, machine learning offers a powerful computational environment to automate analytical modeling and handle tremendous amount of dynamic data streams. By exploiting the remarkable features of Bayesian inference and machine learning, BISHM offers a promising direction to tackle challenging issues encountered in SHM investigation. In this seminar, our recent developments on BISHM and the applications to benchmark SHM projects will be presented.