Techno Press
You logged in as Techno Press

Computers and Concrete
  Volume 14, Number 5, November 2014 , pages 547-561
DOI: https://doi.org/10.12989/cac.2014.14.5.547
 


Factors affecting the properties of recycled concrete by using neural networks
Zhen-Hua Duan and Chi-Sun Poon

 
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.
 
Key Words
    artificial neural networks; compressive strength; elastic modulus; recycled aggregate concrete; recycled aggregate; sensitivity analysis
 
Address
Zhen-Hua Duan and Chi-Sun Poon: Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University,
Hong Kong, China
 

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2024 Techno Press
P.O. Box 33, Yuseong, Daejeon 305-600 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: info@techno-press.com