Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique

Arash Behnia*, Navid Ranjbar, Hwa Kian Chai, Mahyar Masaeli

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

This paper introduces suitable features and methods to define hazard rate function by acoustic emission (AE) parametric analysis to develop robust damage statement index and reliability analysis. AE signal energy was first examined to find out the relation between damage progress and AE signal energy so that a damage index based on AE signal energy could be proposed to quantify progressive damage imposed to ferrocement composite slabs. Moreover, by using AE signal strength, historic index could be computed and utilized to develop a modified hazard rate function through integration of bathtub curve and Weibull function. Furthermore, to provide a practical scheme for real condition monitoring, support vector regression was utilized to produce a robust tools for failure prediction considering uncertainties exist in real structures.

Original languageEnglish
Pages (from-to)823-832
Number of pages10
JournalConstruction and Building Materials
Volume122
Early online date2 Jul 2016
DOIs
Publication statusPublished - 30 Sept 2016

Keywords / Materials (for Non-textual outputs)

  • Acoustic emission
  • Bathtub curve
  • Damage detection
  • Ferrocement slabs
  • Machine learning
  • Reliability analysis

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