Fiber quantity analysis of cementitious composite using multifocus imagery
Source abstract
Abstract
After static and impact loading tests applied to PVA fiber reinforced cementitious composites, depending on the loading type and mixture properties of the composite, different fiber failure types are observed at the fractured surface of test samples. It is very hard to determine the pulled out fiber amount with conventional methods. The purpose of this study is to display these differences in a quantitative way using digital imaging processing techniques and exhibit the relation between absorbed energy and pulled out fiber area. Digital image processing might provide useful techniques to determine structure of the cracked surface of samples. Since close‐up images of the failure surfaces of test samples are needed to examine thin and short fibers, from the point view of science of measurement and modeling from stereo images, digital photogrammetry does not produce prospective results appropriately. In this work, in order to reveal fibers, two complementary image‐processing techniques were used successively. First, images, which obtained from macro photographic lenses at different focal lengths, were combined with multifocus imaging. Second, processed multifocused image is automatically clustered in order to extract fibers with maximum likelihood classification algorithm. Consequently, state of side‐views of the fractured surfaces of composites is drawn precisely. The amount of pulled out fibers were estimated by using the area calculated. The relation between the absorbed energy and pulled fiber area was obtained. With increase of absorbed fracture energy, pulled out fiber amount for both static and dynamic tests increases. POLYM. COMPOS., 39:2126–2134, 2018. © 2016 Society of Plastics Engineers
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Research context
OpenAlex topics: 3D Surveying and Cultural Heritage · Infrastructure Maintenance and Monitoring · Optical measurement and interference techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Engineering, Department of Geomatics Engineering Canakkale Onsekiz Mart University Canakkale
Civil Engineering Faculty, Building Materials Laboratory, Maslak Istanbul Technical University Istanbul
Civil Engineering Faculty, Geomatics Engineering Department, Maslak Istanbul Technical University Istanbul
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