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• W1636452744 abstract "Abstract Due to uncertainties associated with material properties, structural geometry, boundary conditions, and connectivity of structural parts as well as inherent simplifying assumptions in the development of finite element (FE) models, actual behavior of structures often differs from model predictions. FE model updating comprises a multitude of techniques that systematically calibrate FE models in order to match experimental results. Updating of structural models can be posed as an optimization problem where model parameters that minimize the errors between the responses of the model and actual structure are sought. However, due to limited number of experimental responses and measurement errors, the optimization problem may have multiple admissible solutions in the search domain. Global optimization algorithms (GOAs) are useful and efficient tools in such situations as they try to find the globally optimal solution out of many possible local minima, but are not totally immune to missing the right minimum in complex problems such as those encountered in updating. A methodology based on particle swarm optimization (PSO), a GOA, with sequential niche technique (SNT) for FE model updating is proposed and explored in this article. The combination of PSO and SNT enables a systematic search for multiple minima and considerably increases the confidence in finding the global minimum. The method is applied to FE model updating of a pedestrian cable‐stayed bridge using modal data from full‐scale dynamic testing." @default.
• W1636452744 created "2016-06-24" @default.
• W1636452744 creator A5016177150 @default.
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• W1636452744 date "2014-09-09" @default.
• W1636452744 modified "2023-10-14" @default.
• W1636452744 title "Particle Swarm Optimization with Sequential Niche Technique for Dynamic Finite Element Model Updating" @default.
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• W1636452744 doi "https://doi.org/10.1111/mice.12100" @default.
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