Reliability Assessment of a Bridge Structure Subjected to Chloride Attack

Bernt J. Leira, Sebastian Thöns, Michael Havbro Faber Nielsen

Research output: Contribution to journalJournal articleResearchpeer-review

2 Citations (Scopus)

Abstract

Prediction of the service lifetime of concrete structures with respect to chloride
ingress involves a number of parameters that are associated with large
uncertainties. Hence, full-scale measurements are strongly in demand. This
paper begins by summarizing statistical distributions based on measurements
taken from the Gimsøystraumen Bridge in Norway. A large number of
chloride profiles are available based on concrete coring samples, and for each
of these profiles the diffusion coefficient and surface concentration (due to sea
spray) are estimated. Extensive measurements of the concrete cover depth are
also performed. The probability distributions are input into a prediction model
for chloride concentration at the steel reinforcement. By also introducing the
critical chloride concentration as a random variable, the probability of
exceeding the critical threshold is determined as a function of time. To address
chloride attack on the entire bridge, a system model with 90 components is
introduced. Reliability updating based on observations at multiple sites along
the bridge is also investigated. First-order reliability methods typically become
inaccurate for large systems of this type, so an enhanced Monte Carlo
simulation method is applied. It is shown that the corresponding computation
time is significantly reduced compared to crude Monte Carlo methods.
Original languageEnglish
JournalStructural Engineering International
Volume28
Issue number3
Pages (from-to)318-324
ISSN1016-8664
DOIs
Publication statusPublished - 2018
Externally publishedYes

Bibliographical note

This work was supported by Cost Action TU1402: Quantifying the Value of Structural Health Monitoring [Grant number COST-STSM-ECOST-STSM-TU1402-011016-080708].

Keywords

  • Chloride ingress
  • Bridge test data
  • System reliability
  • Enhanced Monte Carlo Method

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