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.
This research demonstrates the use of Digital Image Correlation (DIC) as a non-contact, nondestructive testing and evaluation (NDT&E) technique by presenting experimental results pertinent to damage monitoring and quantification in several material systems at different length scales of interest. At the microstructural level compact tension aluminum alloy specimens were tested under Mode I loading conditions using an appropriate field of view to track grain scale crack initiation and growth. The results permitted the quantification of the strain accumulation near the tip of the fatigue pre-crack, as well as the computation of the relevant crack opening displacement as a function of crack length. At the mesoscale level, damage quantification in fiber reinforced composites subject to both tensile and fatigue loading conditions was achieved by using the DIC as part of a novel integrated NDT approach combining both acoustic and thermal methods. DIC in these experiments provided spatially resolved and high accuracy strain measurements capable to track the formation of damage "hot spots" that corresponded to the sites of the ultimately visible fracture pattern, while it further allowed the correlation of mechanical parameters to thermal and acoustic features. Finally, at the macrostructural level DIC measurements were also performed and compared to traditional displacement gauges mounted on a steel deck model subject to both static and dynamic loads, as well as on masonry structures including hollow and grouted concrete walls.
Fiber-reinforced metal laminate (FRML) composites are currently used as a structural material in the aerospace industry. A common FRML, glass layered aluminum reinforced epoxy (Glare), possesses a set of mechanical properties which was achieved by designing its layup structure to combine metal alloy and fiber-reinforced polymer phases. Beyond static and dynamic mechanical properties at the material characterization phase, however, the need exists to develop methods that could assess the evolving material state of Glare, especially in a progressive failure context. This paper presents a nondestructive approach to monitor the damage at the material scale and combine such information with characterization and postmortem evaluation methods, as well as data postprocessing to provide an assessment of the failure process during monotonic loading conditions. The approach is based on multiscale sensing using the acoustic emission (AE) method, which was augmented in this paper in two ways. First, by applying it to all material components separately in addition to actual Glare specimens. Second, by performing testing and evaluation at both the laboratory scale as well as at the scale defined inside the scanning electron microscopy. Such elaborate testing and nondestructive evaluation results provided the basis for the application of digital signal processing and machine learning methods which were capable to identify data trends that are shown to be correlated with the evolution of failure modes in Glare.
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