A Caltech Library Service

Real-Time Bayesian Damage Detection for Uncertain Dynamical Systems

Ching, Jianye and Beck, James L. and Porter, Keith A. (2004) Real-Time Bayesian Damage Detection for Uncertain Dynamical Systems. In: Proceedings of the 17th ASCE Engineering Mechanics Conference. University of Delaware , Newark, DE.

Full text is not posted in this repository.

Use this Persistent URL to link to this item:


This paper introduces a new Bayesian state-estimation methodology based on stochastic simulation of damage detection for nonlinear structural systems with non-Gaussian uncertainties. The new method uses a linear system with Gaussian uncertainties to build up an importance sampling probability density function (PDF). Samples are taken from the importance sampling PDF to estimate the state of the nonlinear system. The sampled system state can then be used to detect and assess structural and non-structural damage through fragility functions. We demonstrate the consistency of the new methodology using a numerical example and apply the new technique to a real-data case study for damage detection. It is concluded that the proposed method should be useful for real-time damage detection.

Item Type:Book Section
Additional Information:The authors would like to acknowledge the support of the CUREE-Kajima Phase V Joint Research Program and the Caltech George W. Housner Postdoctoral Fellowship.
Funding AgencyGrant Number
CUREE-Kajima Phase V Joint Research ProgramUNSPECIFIED
Subject Keywords:Damage detection, Dynamical systems, Monte Carlo simulation, Particle filters, Bayesian analysis, State estimation, Importance sampling
Record Number:CaltechAUTHORS:20120912-151854049
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:34041
Deposited By: Carmen Nemer-Sirois
Deposited On:15 Nov 2012 19:53
Last Modified:03 Oct 2019 04:15

Repository Staff Only: item control page