Updating models and their uncertainties

Developing practical damage detection and localization schemes is still an area of active research and there exists a large array of techniques.

Structural engineers typically implement and test SHM algorithms in a higher level language such as C/Matlab.

New formulae for Markov chain convergence assessment are derived.

The effectiveness of the proposed approach for Bayesian model updating of structural dynamic models with many uncertain parameters is illustrated with a simulated data example involving a ten-story building that has 31 model parameters to be updated.

However, while our current implementation does not do this, we believe this algorithm is amenable to an implementation that uses local computation. =-=[36]-=- showed that using auto-correlation of the impulse response rather than the impulse response itself can lead to more robust estimates of the mode shapes. A fully probabilistic Bayesian model updating approach provides a robust and rigorous framework for these applications du ..." Abstract: In recent years, Bayesian model updating techniques based on measured data have been applied to system identification of structures and to structural health monitoring.

A fully probabilistic Bayesian model updating approach provides a robust and rigorous framework for these applications due to its ability to characterize modeling uncertainties associated with the underlying structural system and to its exclusive foundation on the probability axioms.

Financial support by the National Science Foundation under subcontract to grant CMS- 9503370 and the Hong Kong Research Grant Council under grants HKUST 639/95E and 6041 /97E is gratefully acknowledged.

However, in general, the efficiency of these proposed approaches is adversely affected by the dimension of the model parameter space.

From this perspective, model updating is viewed as part of robust analysis where modeling uncertainties are explicitly addressed in the analysis of a system.

The exact expressions for updated model predictions are given by multi-dimensional integrals whose direct evaluation is usually computationally prohibitive.

Structural health monitoring (SHM) is an important application area for wireless sensor networks.

Recent work has examined the design of wireless sensor networks for structural data acquisition systems.

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The importance of uncertainty quantification and optimization technique ..." This article presents a brief survey on some of the most relevant developments in the field of optimization under uncertainty.

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