Computational & Technology Resources
an online resource for computational,
engineering & technology publications
PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TO CIVIL AND STRUCTURAL ENGINEERING
Edited by: B.H.V. Topping and B. Kumar
Prediction of Workability of Concrete incorporating Metakaolin and PFA using Neural Networks
J. Bai, S. Wild, A. Ware and B.B. Sabir
School of Technology, University of Glamorgan, Pontypridd, United Kingdom
J. Bai, S. Wild, A. Ware, B.B. Sabir, "Prediction of Workability of Concrete incorporating Metakaolin and PFA using Neural Networks", in B.H.V. Topping, B. Kumar, (Editors), "Proceedings of the Sixth International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 22, 2001. doi:10.4203/ccp.74.22
Keywords: neural network, modelling, prediction, concrete workability, metakaolin, pulverised-fuel ash.
Concrete has many advantages over other construction materials including low cost, adaptability and applicability under many conditions. The world production of cement has greatly increased since 1980. However, cement production generates a large amount of CO2 which damages the environment. Increasing use of by-products, such as Pulverised-fuel ash (PFA), to partially replace Portland cement (PC) in concrete not only reduces the amount of cement used, hence reducing the emission of CO2 and conserving existing resources, but significantly enhances the properties of concrete. Inclusion of PFA in concrete greatly improves workability. Research work done on Metakalin (MK) has shown that the partial replacement of PC with MK in concrete significantly enhances early strength. However MK, unlike PFA, results in increasing water demand with increasing replacement level due to its high chemical activity and high specific surface. The investigation into optimising the contributory effects of PFA and MK on concrete workability by introducing them in combination has been carried out by Bai et al . The research results have shown that loss of workability due to the presence of MK can be compensated for by the incorporation of PFA. The degree of restoration of workability, provided by PFA, is influenced significantly by the cement replacement level. Comprehensive experimentally determined workability data on PC-PFA-MK blends were enabled a comparison to be made of the effect of various replacement levels on the workability. It would be more useful if trends can be extrapolated to develop reliable predictive models. Such models are very important as they help designers to select PFA-MK combinations in performance terms. Thus, there is a need to explore existing data and develop models capable of learning from historical data and to predict concrete workability (slump, compacting factor and Vebe time) in a reliable but economic manner.
This paper presents neural networks for the prediction of workability of concrete incorporating MK and 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 by graduated replacement levels of up to 15% metakaolin and 40 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.
The following conclusions may be drawn from the results of the work:
purchase the full-text of this paper (price £20)