A robust stochastic model updating method with resampling processing

Abstract

Abstract A robust stochastic model updating framework is developed for a better estimation of uncertain properties of parameters. In this framework, in order to improve the robustness, a resampling process is primarily designed for dealing with the ill sample point, especially for limited sample size problems. Next, a mean distance uncertainty qualification metric is proposed based on the Bhattacharyya distance and the Euclidian distance to fully exploit available information from the measurements. The Particle Swarm Optimization algorithm is subsequently employed to update the input parameters of the investigated structure. Finally, the mass-spring system and the steel plate structures are presented to illustrate the effectiveness and advantages of this proposed method. Discussions on the role of the resampling process have been made through using the measured samples added an ill sample.