Factors affecting the properties of recycled concrete by using neural networks

Zhen Hua Duan, Chi Sun Poon

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)547-561
Number of pages15
JournalComputers and Concrete
Volume14
Issue number5
DOIs
Publication statusPublished - 1 Nov 2014

Keywords

  • Artificial neural networks
  • Compressive strength
  • Elastic modulus
  • Recycled aggregate
  • Recycled aggregate concrete
  • Sensitivity analysis

ASJC Scopus subject areas

  • Computational Mechanics

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