Probabilistic capacity models and fragility estimates for RC columns retrofitted with FRP composites

Armin Tabandeh, Paolo Gardoni

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a probabilistic formulation to assess the effectiveness of the fiber reinforced polymer (FRP) retrofit schemes in enhancing the structural performance of reinforced concrete (RC) bridge columns. Two probabilistic models are proposed to predict the deformation capacities of retrofitted columns. One deformation model corresponds to the flexural failure and the other considers the bond failure in the lap-splice region. A Markov Chain Monte Carlo (MCMC) simulation method is used to estimate unknown model parameters in the context of a Bayesian updating approach. The probabilistic capacity models are used to estimate the fragility curves of three example columns. In this paper, fragility is defined as the conditional probability of failure for given deformation demand. The results compare the column fragilities before and after the application of the retrofit measure. The results from the example columns indicate that the use of FRP composites considerably reduced the fragility for the bond failure mode and is also beneficial but with a moderate impact when considering the flexural failure.

Original languageEnglish (US)
Pages (from-to)13-22
Number of pages10
JournalEngineering Structures
Volume74
DOIs
StatePublished - Sep 1 2014

Keywords

  • Bayesian approach
  • Bridge column
  • FRP retrofit
  • Fragility
  • Markov Chain Monte Carlo (MCMC)
  • Probability

ASJC Scopus subject areas

  • Civil and Structural Engineering

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