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Fatigue life prediction of steel bridges using a small scale monitoring system
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.ORCID iD: 0000-0002-2833-4585
2018 (English)Report (Other academic)
Abstract [en]

With an increasing number of bridges approaching their expected service life, improved and new methods for accurate assessment methods are called for. Economical restraints and sustainability reasons will not allow bridge managers to replace the numerous bridges that theoretically will be judged unsafe. As a method for refined assessment, in-service monitoring can be used to accurately determine the actual structural response. This will enable an alleviation of conservative estimates and facilitate accurate service life predictions.For fatigue assessment, the well established technique for strain measurements using electrical strain gauges can provide accurate estimations of the actual structural response. It is, however, not possible to mount gauges at all positions with critical details for large structures as bridges. The possibility of using a small scale monitoring system with few sensors has been investigated and a review of methods for predicting the response at unmeasured locations is presented in this report. A few selected methods, like multivariate regression and artificial neural networks (ANN), have been tested and evaluated on measured data from the Rautasjokk Bridge.The use of an ANN for time history prediction is demonstrated and promising results are presented. However, the predictions are sensitive to the input data and questionable results were attained when the input deviated from the training set. For predictions based on stress range spectra, multivariate linear regression constitute a robust tool and provided a high accuracy for an example from the Rautasjokk Bridge.This report also contains a presentation of the monitoring campaign of the Rautasjokk Bridge. The setup of the system and the management of data are described. The bridge is used for demonstrating the prediction methods and an advanced assessment approach based on linear elastic fracture mechanics. It enables a consideration of the measured response and a reliability based updating considering inspection results.

Place, publisher, year, edition, pages
Stockholm, 2018. , p. 55
Keywords [en]
Fatigue, Monitoring, Bridges
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-238718OAI: oai:DiVA.org:kth-238718DiVA, id: diva2:1261925
Funder
Swedish Transport Administration, TRV 2015/50535
Note

QC 20181119

Available from: 2018-11-08 Created: 2018-11-08 Last updated: 2018-11-19Bibliographically approved

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fulltext(8976 kB)69 downloads
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Leander, John

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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