Estimation of concrete carbonation depth considering multiple influencing factors on the deterioration of durability for reinforced concrete structures

Hae Chang Cho, Hyunjin Ju, Jae Yuel Oh, Kyung Jin Lee, Kyung Won Hahm, Kang Su Kim

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

While the durability of concrete structures is greatly influenced by many factors, previous studies typically considered only a single durability deterioration factor. In addition, these studies mostly conducted their experiments inside the laboratory, and it is extremely hard to find any case in which data were obtained from field inspection. Accordingly, this study proposed an Adaptive Neurofuzzy Inference System (ANFIS) algorithm that can estimate the carbonation depth of a reinforced concrete member, in which combined deterioration has been reflected based on the data obtained from field inspections of 9 buildings. The proposed ANFIS algorithm closely estimated the carbonation depths, and it is considered that, with further inspection data, a higher accuracy would be achieved. Thus, it is expected to be used very effectively for durability estimation of a building of which the inspection is performed periodically.

Original languageEnglish
Article number4814609
JournalAdvances in Materials Science and Engineering
Volume2016
DOIs
Publication statusPublished - Jan 1 2016
Externally publishedYes

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

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