Determining the Best Pareto-solution in a Multi-Objective Approach for Model Updating
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Bibliografische Angaben
| Autor(en): |
María Infantes
(Department of Mechanics, Universidad de Córdoba, Cordoba, Spain)
Javier Naranjo-Pérez (Department of Continuum Mechanics, Universidad de Sevilla, Seville, Spain) Andrés Sáez 
(Department of Continuum Mechanics, Universidad de Sevilla, Seville, Spain)
Javier F. Jiménez-Alonso (Department of Building Structures, Universidad de Sevilla, Seville, Spain) |
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| Medium: | Tagungsbeitrag | ||||
| Sprache(n): | Englisch | ||||
| Tagung: | IABSE Symposium: Towards a Resilient Built Environment Risk and Asset Management, Guimarães, Portugal, 27-29 March 2019 | ||||
| Veröffentlicht in: | IABSE Symposium Guimarães 2019 | ||||
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| Seite(n): | 523-530 | ||||
| Anzahl der Seiten (im PDF): | 8 | ||||
| DOI: | 10.2749/guimaraes.2019.0523 | ||||
| Abstrakt: |
Using a multi-objective optimization algorithm avoid the use of weighting factors to balance the different residuals in a finite element model updating procedure under the maximum likelihood method. By using this approach, the fittest model is not unique and a set of solutions that form a curve, so-called Pareto optimal front, is obtained. Within this paper, first a review of the state of the art on the criteria used to determine the most adequate model among all the solutions of the Pareto front is presented. Subsequently, a case study of a real footbridge is considered. A finite element model of the footbridge is updated based on its experimental modal parameters. The Non- Dominated Sorting Genetic Algorithm is used to obtain the Pareto front. Since all the solutions in the Pareto front are non-dominated, the selection of the best candidate requires a reasonable criterion. Herein, different procedures to select the best updated model are discussed. |
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| Stichwörter: |
Fußgängersteg Fußgängerbrücke Entscheidungsfindung
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