Skip to main navigation Skip to search Skip to main content

Probabilistic generalization of a comprehensive model for the deterioration prediction of RC structure under extreme corrosion environments

  • Xingji Zhu
  • , Zaixian Chen*
  • , Hao Wang
  • , Yabin Chen
  • , Longjun Xu
  • *Corresponding author for this work
  • Harbin Institute of Technology Weihai
  • Southeast University, Nanjing
  • Cooperative Innovation Center of Engineering Construction and Safety in Shandong Blue Economic Zone

Research output: Contribution to journalArticlepeer-review

Abstract

In some extreme corrosion environments, the erosion of chloride ions and carbon dioxide can occur simultaneously, causing deterioration of reinforced concrete (RC) structures. This study presents a probabilistic model for the sustainability prediction of the service life of RC structures, taking into account that combined deterioration. Because of the high computational cost, we also present a series of simplifications to improve the model. Meanwhile, a semi-empirical method is also developed for this combined effect. By probabilistic generalization, this simplified method can swiftly handle the original reliability analysis which needs to be based on large amounts of data. A comparison of results obtained by the models with and without the above simplifications supports the significance of these improvements.

Original languageEnglish
Article number3051
JournalSustainability (Switzerland)
Volume10
Issue number9
DOIs
StatePublished - 28 Aug 2018
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Carbonation
  • Chloride ingress
  • Corrosion
  • Probabilistic
  • Reinforced concrete
  • Sustainability prediction

Fingerprint

Dive into the research topics of 'Probabilistic generalization of a comprehensive model for the deterioration prediction of RC structure under extreme corrosion environments'. Together they form a unique fingerprint.

Cite this