Dynamic Bayesian network for durability of reinforced concrete structures in long-term environmental exposures

Hongyuan Guo, You Dong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Reinforced concrete (RC) structures under the marine environment may be subjected to chloride-induced corrosion of reinforcement, which significantly impacts the structural serviceability and reliability and further affects the sustainability and development of society. However, most of the existing durability assessment methods for RC structures only address their static and deterministic durability prediction and assessment at the design stage given the constant environment, ignoring the influences of stochastic environmental effects, uncertainties in structural properties, and inspection results. To this end, this paper proposes a dynamic Bayesian network (DBN) based durability assessment framework combined with a deterioration model that considers random changes in environmental parameters, convective chloride ion transport, and corrosion-induced cracking of concrete. In this framework, two-dimensional chloride transport and its influences on the durability deterioration assessment are concerned and achieved using the finite difference method. Besides, to reduce the deviations in probabilistic evaluation, the good-lattice-point-set-partially stratified-sampling (GLP-PSS) method is employed to establish a DBN framework. The proposed DBN framework is used for sensitivity analysis through a real-world example to examine the effects of the environmental model, chloride transport mode, and inspection results of concrete crack on durability assessment.

Original languageEnglish
Article number106821
JournalEngineering Failure Analysis
Volume142
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Durability assessment
  • Dynamic Bayesian Network
  • Environmental actions
  • Reinforced concrete (RC) structures

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

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