Prediction of workability of concrete incorporating metakaolin and PFA using neural networks

  • Authors:

  • J. Bai;S. Wild;A. Ware;B. B. Sabir

  • Affiliations:

  • School of Technology, University of Glamorgan, Pontypridd, United Kingdom;School of Technology, University of Glamorgan, Pontypridd, United Kingdom;School of Technology, University of Glamorgan, Pontypridd, United Kingdom;School of Technology, University of Glamorgan, Pontypridd, United Kingdom

  • Venue:
  • ICAAICSE '01 Proceedings of the sixth international conference on Application of artificial intelligence to civil & structural engineering
  • Year:
  • 2001

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Abstract

This paper presents neural networks for the prediction of workability of concrete incorporating metakaolin (MK) and pulverised-fuel ash (PFA). The neural network models are validated using independent data sets and give high prediction accuracy. The predictions produced reflect the effect of variations in pozzolanic replacement of portland cement (PC) by graduated replacement levels of up to 15% metakaolin and 40% PFA. The results show that the models developed are reliable and accurate and they can be used to predict the workability parameters of slump, compacting factor and Vebe time across a wide range of Portland cement-PFA-MK. These demonstrate that using neural networks to predict concrete workability is practical and beneficial.