Neural network based hygrothermal prediction for deterioration risk analysis of surface-protected concrete façade element

Woubishet Zewdu Taffese*, Esko Sistonen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

22 Citations (Scopus)

Abstract

Accurate prediction of hygrothermal behavior in the concrete is vital requirements to make more realistic service-life extension decisions. In this work, a neural network based hygrothermal prediction model to estimate a temporal hygrothermal condition in surface-protected concrete façade members is developed and presented. The model learns the case-specific features of hygrothermal behavior using the two years temperature and relative humidity data obtained from the installed probes. The performance evaluation confirms that the model describes the hygrothermal behavior inside the concrete façade with a high accuracy. This in turn enables to assess the corrosion rate as well as deterioration risk levels caused by frost and chemical attacks while identifying the appropriate surface protection system.

Original languageEnglish
Pages (from-to)34-48
Number of pages15
JournalConstruction and Building Materials
Volume113
DOIs
Publication statusPublished - 15 Jun 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • Chemical attack
  • Concrete façade
  • Corrosion rate
  • Deterioration
  • Frost attack
  • Hygrothermal
  • In-service monitoring
  • Modelling
  • Neural network
  • Surface protection

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