Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2500
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dc.contributor.authorSahoo, K-
dc.contributor.authorSarkar, P-
dc.contributor.authorRobin Davis, P-
dc.date.accessioned2016-05-02T04:23:50Z-
dc.date.available2016-05-02T04:23:50Z-
dc.date.issued2016-03-
dc.identifier.citationInternational Conference on Environment, Agricultural and Civil Engineering (ICEACE-2016) London, UK 24-25 March 2016en_US
dc.identifier.urihttp://hdl.handle.net/2080/2500-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractRecycled coarse aggregates (RCA) are different in properties and composition than natural coarse aggregate. So it is very difficult to expect the same behaviour of RCA during mix design of concrete. This paper attempts to reveal the probable relationship between RCA properties and compressive strength of RCA concrete through regression analysis based on 20 numbers of available data collected from published literature. The parameters considered are water, cement, natural coarse aggregate, sand, RCA content in the design mix, saturated surface dried density, and maximum size of RCA. The regression analysis is run through SPSS software. It is observed that proposed relation predicts the compressive strength of RCA concrete that closely matches the experimental results.en_US
dc.subjectCompressive strengthen_US
dc.subjectConcreteen_US
dc.subjectRecycled coarse aggregateen_US
dc.subjectRegression analysisen_US
dc.titleArtificial Neural Networks for Prediction of Compressive Strength of Recycled Aggregate Concreteen_US
dc.typeArticleen_US
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