Using neural networks to determine the significance of aggregate characteristics affecting the mechanical properties of recycled aggregate concrete

Zhenhua Duan, Shaodan Hou, Chi Sun Poon, Jianzhuang Xiao, Yun Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)

Abstract

It has been proved that artificial neural networks (ANN) can be used to predict the compressive strength and elastic modulus of recycled aggregate concrete (RAC) made with recycled aggregates from different sources. This paper is a further study of the use of ANN to analyze the significance of each aggregate characteristic and determine the best combinations of factors that would affect the compressive strength and elastic modulus of RAC. The experiments were carried out with 46 mixes with several types of recycled aggregates. The experimental results were used to build ANN models for compressive strength and elastic modulus, respectively. Different combinations of factors were selected as input variables until the minimum error was reached. The results show that water absorption has the most important effect on aggregate characteristics, further affecting the compressive strength of RAC, and that combined factors including concrete mixes, curing age, specific gravity, water absorption and impurity content can reduce the prediction error of ANN to 5.43%. Moreover, for elastic modulus, water absorption and specific gravity are the most influential, and the network error with a combination of mixes, curing age, specific gravity and water absorption is only 3.89%.

Original languageEnglish
Article number2171
JournalApplied Sciences (Switzerland)
Volume8
Issue number11
DOIs
Publication statusPublished - 6 Nov 2018

Keywords

  • Aggregate characteristic
  • Artificial neural networks
  • Input variable
  • Recycled aggregate
  • Recycled aggregate concrete

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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