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Model parameter estimation using Bayesian and deterministic approaches: the case study of the Maddalena Bridge

De Falco, A; Girardi, M; Pellegrini, D; Robol, L; Sevieri, G; (2018) Model parameter estimation using Bayesian and deterministic approaches: the case study of the Maddalena Bridge. In: Bartoli, G and Betti, M and Fagone, M and Pintucchi, B, (eds.) (Proceedings) 14th International Conference on Building Pathology and Constructions Repair (CINPAR). (pp. pp. 210-217). ELSEVIER SCIENCE BV Green open access

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Abstract

Finite element modeling has become common practice for assessing the structural health of historic constructions. However, because of the uncertainties typically affecting our knowledge of the geometrical dimensions, material properties and boundary conditions, numerical models can fail to predict the static and dynamic behavior of such structures. In order to achieve more reliable predictions, important information can be obtained measuring the structural response under ambient vibrations. This wholly non-destructive technique allows obtaining very accurate information on the structure’s dynamic properties (Brincker and Ventura (2015)). Moreover, when experimental data is coupled with a finite element model, an estimate of the boundary conditions and the mechanical properties of the constituent materials can also be obtained via model updating procedures. This work presents two different model updating procedures. The first relies on construction of local parametric reduced-order models embedded in a trust region scheme to minimize the distance between the natural frequencies experimentally determined and the corresponding numerically evaluated ones (Girardi et al. (2018)). The second has been developed within a Bayesian statistical framework and uses both frequencies and mode shapes (Yuen (2015)). Both algorithms are used in conjunction with the NOSA-ITACA code for calculation of the eigenfrequencies and eigenvectors. These procedures are illustrated in the case study of the medieval Maddalena Bridge in Borgo a Mozzano (Italy). Experimental data, frequencies and mode shapes, acquired in 2015 (Azzara et al. (2017)) have enabled calibration of the bridge’s constituent materials and boundary conditions

Type: Proceedings paper
Title: Model parameter estimation using Bayesian and deterministic approaches: the case study of the Maddalena Bridge
Event: 14th International Conference on Building Pathology and Constructions Repair (CINPAR)
Location: Florence, Italy
Dates: 20 - 22 June 2018
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.prostr.2018.11.028
Additional information: This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, and provide a link to the Creative Commons license. You do not have permission under this license to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Keywords: Model updating; FEM; Trust region method; Proxy models; Frequency optimization; Bayesian updating; Masonry bridges
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10073963
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