Please use this identifier to cite or link to this item:
Title: Artificial Neural Networks for Prediction of Compressive Strength of Recycled Aggregate Concrete
Authors: Sahoo, K
Sarkar, P
Robin Davis, P
Keywords: Compressive strength
Recycled coarse aggregate
Regression analysis
Issue Date: Mar-2016
Citation: International Conference on Environment, Agricultural and Civil Engineering (ICEACE-2016) London, UK 24-25 March 2016
Abstract: Recycled 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.
Description: Copyright belongs to proceeding publisher
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2016_ICEACE_Sahoo_Artifiscial.pdf678.33 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.