Journal article
A data fusion approach for progressive damage quantification in reinforced concrete masonry walls
Smart materials and structures, v 23(1)
10 Dec 2013
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- A data fusion approach for progressive damage quantification in reinforced concrete masonry walls
- Creators
- Prashanth Abraham Vanniamparambil - Drexel UniversityMohammad Bolhassani - Drexel UniversityRami Carmi - Drexel UniversityFuad Khan - Drexel UniversityIvan Bartoli - Drexel UniversityFranklin L Moon - Drexel UniversityAhmad Hamid - Drexel UniversityAntonios Kontsos - Drexel University
- Publication Details
- Smart materials and structures, v 23(1)
- Publisher
- IOP Publishing
- Number of pages
- 11
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering; Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:000328571000009
- Scopus ID
- 2-s2.0-84891435414
- Other Identifier
- 991019168469704721
UN Sustainable Development Goals (SDGs)
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Instruments & Instrumentation
- Materials Science, Multidisciplinary