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dc.creatorPapadioti, D. C.en
dc.creatorPapadimitriou, C.en
dc.description.abstractFinite element (FE) model updating and validation techniques are formulated as single and multi-objective optimization problems. A multi-objective optimization framework results in multiple Pareto optimal models that are consistent with the measured data and the residuals used to measure the discrepancies between the measured and the FE model predicted characteristics. The uncertainty in the Pareto optimal models can then be propagated to predict the uncertainty in the response predictions. Gradient-based optimization algorithms, such as the Normal Boundary Intersection algorithm, are used to compute the Pareto optimal solutions. These iterative algorithms require repeated solutions of the FE model for various values of the model parameters, as well as repeated computation of the gradients of the response characteristics involved in the residuals. For FE models with very high number of degrees of freedom, of the order of millions, repeated solutions of the FE models can be computationally very demanding. Component mode synthesis (CMS) methods are integrated into the updating method in order to reduce the computational effort required for performing the single- and multi-objective optimization problems. Exploiting certain schemes often en-countered in FE model parameterization, it is shown that CMS allows the repeated computations to be carried out efficiently in a significantly reduced space of generalized coordinates, avoiding the solution of the fixed-interface/constrained modes and the assembling of reduced system matrices at each iteration. The final computational cost is associated with that of estimating the response characteristics of the reduced system at each iteration.en
dc.subjectComponent mode synthesisen
dc.subjectModel updatingen
dc.subjectMulti-objective optimizationen
dc.subjectStructural identificationen
dc.subjectComputational costsen
dc.subjectComputational efforten
dc.subjectDynamic reductionen
dc.subjectFE modelen
dc.subjectFinite element modelsen
dc.subjectGeneralized coordinatesen
dc.subjectGradient-based optimizationen
dc.subjectIterative algorithmen
dc.subjectMeasured dataen
dc.subjectModel parametersen
dc.subjectMulti objectiveen
dc.subjectMulti-objective optimization problemen
dc.subjectNormal boundary intersectionsen
dc.subjectNumber of degrees of freedomen
dc.subjectOptimization frameworken
dc.subjectPareto optimal solutionsen
dc.subjectReduced spaceen
dc.subjectReduced systemsen
dc.subjectResponse characteristicen
dc.subjectResponse predictionen
dc.subjectCivil engineeringen
dc.subjectComputational methodsen
dc.subjectEngineering geologyen
dc.subjectModal analysisen
dc.subjectMultiobjective optimizationen
dc.subjectPareto principleen
dc.subjectStructural analysisen
dc.subjectStructural dynamicsen
dc.subjectStructural optimizationen
dc.subjectFinite element methoden
dc.titleFinite element model validation and predictions using dynamic reduction techniquesen

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