Title

Use Of Family Of Models For Performance Predictions And Decision Making

Keywords

FEM; Load rating; Movable bridge; Prediction; Reliability; SHM; Structural identification; Uncertainty

Abstract

The issue about performance prediction is the need of a well representative model which can be analytical or mathematical due to non-availability of the future data. Analytical or mathematical models can be identified with the help of current data coming from structural health monitoring (SHM) systems and these models can be used for future performance predictions by incorporating uncertainties coming from modeling and monitoring data. In this study, a well calibrated finite element model (FEM) of a real life structure, which is accepted as the parent model of the family models, is introduced. Based on this model, offspring models, which include the modeling and measurement uncertainties are generated. In the offspring generation process uncertainties such as boundary conditions, loads, geometric and mechanical properties of the elements are defined with distributions. After the generation process, offspring models are analyzed and set of results are obtained for a family of models. These results are used for structural reliability calculations in the performance prediction part of the paper. At this point, the other important considerations such as system model definition and correlation of the components for the system reliability approach are also taken into account. Finally, future performances in the case of instantaneous or continuous structural changes are considered for structural system reliability prediction, which is critical for decision making about future performance of the structure, by incorporating uncertainties from measurement through modeling on the movable bridge. © The Society for Experimental Mechanics, Inc. 2012.

Publication Date

6-6-2012

Publication Title

Conference Proceedings of the Society for Experimental Mechanics Series

Volume

1

Number of Pages

423-431

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-1-4614-2413-0_42

Socpus ID

84861726148 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84861726148

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