Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/9941
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dc.contributor.authorDaniel Niruban, S.-
dc.contributor.authorSajeevan, M.-
dc.contributor.authorPratheeba, J.-
dc.contributor.authorSathushka Heshan, B.W.-
dc.contributor.authorSathiparan, N.-
dc.date.accessioned2023-12-15T07:27:26Z-
dc.date.available2023-12-15T07:27:26Z-
dc.date.issued2023-
dc.identifier.citationDaniel Niruban Subramaniam, Mohan Sajeevan, Jeyananthan Pratheeba, Sathushka Heshan Bandara Wijekoon & Navaratnarajah Sathiparan (04 Dec 2023): Characterization of the shape of aggregates using image analysis and machine learning classification tools, Geomechanics and Geoengineering, DOI: 10.1080/17486025.2023.2288655en_US
dc.identifier.urihttp://repo.lib.jfn.ac.lk/ujrr/handle/123456789/9941-
dc.description.abstractEngineering applications including pervious concrete require effective packing of aggregates to optimize strength. Size and shape distribution of aggregates significantly affect the performance. Computational methods numerically represent shape of aggregates, from image analysis, the effectiveness of has not been compared and verified. This study aims to analyse the representability of shape aspects of aggregates by different computational methods. Crushed aggregates were grouped into 5 clusters, and each group was milled in a Los Angeles machine for different degrees (0-2000) to induce morphological changes on the aggregates. Aggregates ranging from 5 to 30mm in diameter were obtained (7191 in total). imageJTM, was used to compute dimensions and shape factors of aggregates from 14 computational methods. Statistical tests, Pearson's Correlations and Principal Components Analysis and machine learning classification tools, Decision-tree, Random-Forest, Naive-Bayes, Support-Vector-Machines, K-Nearest-Neighbours and Perceptron were employed to assess. In conclusion, no shape factor could be singularly used to numerically represent the morphological changes on aggregate particles but a combination of shape factors is required. Data matrix had three primary dimensions. Combination of Circularity, Kumbrein-Solidity and Barksdale-Shape-Factor yield best representation of aggregate shape. Regression Tree classification method had the highest accuracy (0.9) in classifying milled and ARTICLE HISTORYen_US
dc.language.isoenen_US
dc.publisherTaylor & Francisen_US
dc.subjectAggregatesen_US
dc.subjectShape factoren_US
dc.subjectMorphologyen_US
dc.subjectShape distributionen_US
dc.subjectAggregate surface textureen_US
dc.titleCharacterization of the shape of aggregates using image analysis and machine learning classification toolsen_US
dc.typeArticleen_US
dc.identifier.doihttps://d0i.org/10.1 080/17486025.2023.2288655en_US
Appears in Collections:Civil Engineering



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