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Multi-fidelity modelling for structural identification

 Multi-fidelity modelling for structural identification
Author(s): ,
Presented at IABSE Symposium: Towards a Resilient Built Environment Risk and Asset Management, Guimarães, Portugal, 27-29 March 2019, published in , pp. 1092-1099
DOI: 10.2749/guimaraes.2019.1092
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Asset-management decision-making is often improved by obtaining a better understanding structural behaviour through monitoring, which can then help avoid unnecessary repair, retrofit and replacemen...
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Bibliographic Details

Author(s): (Swiss Federal Institute of Technology, Lausanne, Switzerland; ETH Zurich, Future Cities Laboratory, Singapore-ETH Centre, Singapore)
(Swiss Federal Institute of Technology, Lausanne, Switzerland; ETH Zurich, Future Cities Laboratory, Singapore-ETH Centre, Singapore)
Medium: conference paper
Language(s): English
Conference: IABSE Symposium: Towards a Resilient Built Environment Risk and Asset Management, Guimarães, Portugal, 27-29 March 2019
Published in:
Page(s): 1092-1099 Total no. of pages: 8
Page(s): 1092-1099
Total no. of pages: 8
DOI: 10.2749/guimaraes.2019.1092
Abstract:

Asset-management decision-making is often improved by obtaining a better understanding structural behaviour through monitoring, which can then help avoid unnecessary repair, retrofit and replacement of existing infrastructure. Interpretation of monitoring data in the presence of biased and systematic uncertainties may require computationally time-consuming numerical models to approximate real structural behaviour. These models could be replaced by less time-consuming machine learning-based surrogate models. When and how this should be done is the subject of current research. In this paper, the use of surrogate models in a multi-fidelity framework for structural identification of a full-scale bridge is presented. The effects of varying degrees of fidelity are studied in a transparent manner within a structural-identification framework. The use of models with multiple fidelities helps obtain accurate model updating results in less time compared with using only one high-fidelity model class for simulations.

Keywords:
model updating Surrogate modelling uncertainty quantification error-domain model falsification