Methodology for the development of bridge-specific fragility curves

Sotiria P. Stefanidou, Andreas J. Kappos

Research output: Contribution to journalArticlepeer-review

120 Scopus citations

Abstract

A new methodology for the development of bridge-specific fragility curves is proposed with a view to improving the reliability of loss assessment in road networks and prioritising retrofit of the bridge stock. The key features of the proposed methodology are the explicit definition of critical limit state thresholds for individual bridge components, with consideration of the effect of varying geometry, material properties, reinforcement and loading patterns on the component capacity; the methodology also includes the quantification of uncertainty in capacity, demand and damage state definition. Advanced analysis methods and tools (nonlinear static analysis and incremental dynamic response history analysis) are used for bridge component capacity and demand estimation, while reduced sampling techniques are used for uncertainty treatment. Whereas uncertainty in both capacity and demand is estimated from nonlinear analysis of detailed inelastic models, in practical application to bridge stocks, the demand is estimated through a standard response spectrum analysis of a simplified elastic model of the bridge. The simplified methodology can be efficiently applied to a large number of bridges (with different characteristics) within a road network, by means of an ad hoc developed software involving the use of a generic (elastic) bridge model, which derives bridge-specific fragility curves.

Original languageBritish English
Pages (from-to)73-93
Number of pages21
JournalEarthquake Engineering and Structural Dynamics
Volume46
Issue number1
DOIs
StatePublished - 1 Jan 2017

Keywords

  • bridges
  • damage states
  • fragility curves
  • loss estimation
  • nonlinear analysis
  • road network
  • uncertainty analysis

Fingerprint

Dive into the research topics of 'Methodology for the development of bridge-specific fragility curves'. Together they form a unique fingerprint.

Cite this