A probabilistic approach for damage identification and crack mode classification in reinforced concrete structures

Alireza Farhidzadeh, Salvatore Salamone, Puneet Singla

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

Abstract

Reinforced concrete is subjected to deterioration due to aging, increased load, and natural hazards. To minimize the maintenance costs and to increase the operation lifetime, researchers and practitioners are increasingly interested in improving current nondestructive evaluation technologies or building advanced structural health monitoring strategies. Acoustic emission methods offer an attractive solution for nondestructive evaluation/structural health monitoring of reinforced concrete structures. In particular, monitoring the development of cracks is of large interest because their properties reflect not only the condition of concrete as material but also the condition of the entire system at structural level. This article presents a new probabilistic approach based on Gaussian mixture modeling of acoustic emission to classify crack modes in reinforced concrete structures. Experimental results obtained in a full-scale reinforced concrete shear wall subjected to reversed cyclic loading are used to demonstrate and validate the proposed approach.

Original languageEnglish (US)
Pages (from-to)1722-1735
Number of pages14
JournalJournal of Intelligent Material Systems and Structures
Volume24
Issue number14
DOIs
StatePublished - Sep 2013

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Mechanical Engineering

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