A data fusion approach for progressive damage quantification in reinforced concrete masonry walls

Prashanth Abraham Vanniamparambil, Mohammad Bolhassani, Rami Carmi, Fuad Khan, Ivan Bartoli, Franklin L. Moon, Ahmad Hamid, Antonios Kontsos

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


This paper presents a data fusion approach based on digital image correlation (DIC) and acoustic emission (AE) to detect, monitor and quantify progressive damage development in reinforced concrete masonry walls (CMW) with varying types of reinforcements. CMW were tested to evaluate their structural behavior under cyclic loading. The combination of DIC with AE provided a framework for the cross-correlation of full field strain maps on the surface of CMW with volume-inspecting acoustic activity. AE allowed in situ monitoring of damage progression which was correlated with the DIC through quantification of strain concentrations and by tracking crack evolution, visually verified. The presented results further demonstrate the relationships between the onset and development of cracking with changes in energy dissipation at each loading cycle, measured principal strains and computed AE energy, providing a promising paradigm for structural health monitoring applications on full-scale concrete masonry buildings.

Original languageEnglish (US)
Article number015007
JournalSmart Materials and Structures
Issue number1
StatePublished - Jan 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Civil and Structural Engineering
  • Atomic and Molecular Physics, and Optics
  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Electrical and Electronic Engineering


  • acoustic emission
  • data fusion
  • digital image correlation
  • reinforced concrete masonry walls
  • structural health monitoring


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