Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data

Download statistics - Document (COUNTER):

Patelli, E.; Govers, Y.; Broggi, M.; Gomes, H.M.; Link, M.; Mottershead, J.E.: Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data. In: Archive of Applied Mechanics 87 (2017), Nr. 5, S. 905-925. DOI: https://doi.org/10.1007/s00419-017-1233-1

Repository version

To cite the version in the repository, please use this identifier: https://doi.org/10.15488/1936

Selected time period:

year: 
month: 

Sum total of downloads: 156




Thumbnail
Abstract: 
Deterministic model updating is now a mature technology widely applied to large-scale industrial structures. It is concerned with the calibration of the parameters of a single model based on one set of test data. It is, of course, well known that different analysts produce different finite element models, make different physics-based assumptions, and parameterize their models differently. Also, tests carried out on the same structure, by different operatives, at different times, under different ambient conditions produce different results. There is no unique model and no unique data. Therefore, model updating needs to take account of modeling and test-data variability. Much emphasis is now placed on what has become known as stochastic model updating where data are available from multiple nominally identical test structures. In this paper two currently prominent stochastic model updating techniques (sensitivity-based updating and Bayesian model updating) are described and applied to the DLR AIRMOD structure.
License of this version: CC BY 4.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2017
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 116 74.36%
2 image of flag of No geo information available No geo information available 6 3.85%
3 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 6 3.85%
4 image of flag of United States United States 5 3.21%
5 image of flag of India India 4 2.56%
6 image of flag of Norway Norway 2 1.28%
7 image of flag of Malaysia Malaysia 2 1.28%
8 image of flag of Italy Italy 2 1.28%
9 image of flag of France France 2 1.28%
10 image of flag of Egypt Egypt 2 1.28%
    other countries 9 5.77%

Further download figures and rankings:


Hinweis

Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.

Search the repository


Browse