Reducing Epistemic Uncertainty Using a Model Updating Cognitive System

Abstract

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).