Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35497
Title: Factors affecting the properties of recycled concrete by using neural networks
Authors: Duan, ZH
Poon, CS 
Keywords: Artificial neural networks
Compressive strength
Elastic modulus
Recycled aggregate
Recycled aggregate concrete
Sensitivity analysis
Issue Date: 2014
Publisher: Techno Press
Source: Computers and concrete, 2014, v. 14, no. 5, p. 547-561 How to cite?
Journal: Computers and concrete 
Abstract: Artificial neural networks (ANN) has been proven to be able to predict the compressive strength and elastic modulus of recycled aggregate concrete (RAC) made with recycled aggregates (RAs) from different sources. However, ANN is itself like a black box and the output from the model cannot generate an exact mathematical model that can be used for detailed analysis. So in this study, sensitivity analysis is conducted to further examine the influence of each selected factor on the output value of the models. This is not only conducive to the determination and selection of the more important factors affecting the results, but also can provide guidance for researchers in adjusting mix proportions appropriately when designing RAC based on the variation of these factors.
URI: http://hdl.handle.net/10397/35497
ISSN: 1598-8198 (print)
1598-818x (online)
DOI: 10.12989/cac.2014.14.5.547
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