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• W2115397797 abstract "To incorporate uncertainty in structural analysis, a knowledge of the uncertainty in the model parameters is required. This paper describes efficient techniques to identify and quantify variability in the parameters from experimental data by maximising the likelihood of the measurements, using the well-established Monte Carlo or perturbation methods for the likelihood computation. These techniques are validated numerically and experimentally on a cantilever beam with a point mass at an uncertain location. Results show that sufficient accuracy is attainable without a prohibitive computational effort. The perturbation approach requires less computation but is less accurate when the response is a highly nonlinear function of the parameters." @default.
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• W2115397797 date "2005-12-01" @default.
• W2115397797 modified "2023-09-30" @default.
• W2115397797 title "Uncertainty identification by the maximum likelihood method" @default.
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• W2115397797 doi "https://doi.org/10.1016/j.jsv.2005.07.006" @default.
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