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Titlebook: Topics in Model Validation and Uncertainty Quantification, Volume 5; Proceedings of the 3 Todd Simmermacher,Scott Cogan,Costas Papadimitrio

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楼主: JAR
发表于 2025-3-23 12:03:20 | 显示全部楼层
2191-5644 ndamental and applied aspects of Structural Dynamics, including papers on:. .Uncertainty Quantification & Propagation in Structural Dynamics.Robustness to Lack of Knowledge in Design.Model Validation.978-1-4899-9604-6978-1-4614-6564-5Series ISSN 2191-5644 Series E-ISSN 2191-5652
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Remaining Fatigue Life Predictions Considering Load and Model Parameters Uncertainty,ed to cyclic and/or random operational loads during their service life. Therefore, monitoring the critical components of these systems, assessing their structural integrity, recursively predicting their remaining fatigue life (RFL), and providing a cost-efficient reliability-based inspection and mai
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Fast Computing Techniques for Bayesian Uncertainty Quantification in Structural Dynamics,tegrated with the Bayesian framework to efficiently handle large-order models of hundreds of thousands or millions degrees of freedom and localized nonlinear actions activated during system operation. Fast and accurate component mode synthesis (CMS) techniques are proposed, consistent with the finit
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Considering Wave Passage Effects in Blind Identification of Long-Span Bridges,l conditions, as well as loss of coherency in arriving waves. These spatial variations can drastically influence the dynamic response; hence, this phenomenon must be considered in any vibration-based identification method. There are numerous Multi-Input Multi-Output (MIMO) identification techniques
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Quantification of Parametric Model Uncertainties in Finite Element Model Updating Problem via Fuzzy Numbers, knowledge about the physical parameters that control the actual behavior. In this sense, parametric identification of civil engineering structures using uncertain numerical models should be subject to a particular interest in terms of accuracy and reliability of identified models. In this study, mo
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Quantifying Maximum Achievable Accuracy of Identified Modal Parameters from Noise Contaminated Free Vibration Data,sian white noise. These limits are found through the Cramer-Rao lower bound. The paper compares the exact findings with previous approximate results found in the literature. Comparisons are drawn with results from stochastic simulations of a Fourier domain approach for estimation of natural frequenc
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