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A robust stochastic model updating method with resampling processing
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DOI：10.1016/j.ymssp.2019.106494.png)
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摘要
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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. (C) 2019 Elsevier Ltd. All rights reserved.
Keyword:
Stochastic model updating
Robustness
Resampling
Bhattacharyya distance
Euclidian distance
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期刊
IF:
8.9
论文数:
1.3W
被引数:
6.6W
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A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation基于响应面模型和蒙特卡罗模拟的参数变异性量化随机模型修正方法
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