%0 Journal Article
%A Awoyera, P. O.
%D 2017
%F eprints:8704
%I Taylor & Francis
%J Materials Research Innovations
%P 1-7
%T Predictive models for determination of  compressive and split-tensile strengths of steel  slag aggregate concrete
%U http://eprints.covenantuniversity.edu.ng/8704/
%X The current study adopts the results of completed laboratory experiments for modelling the strength  properties of steel slag aggregate concrete using artificial neural network (ANN) technique. Note  worthily, the considered experiments reported that strength properties increased with increasing  curing age. The variation of factors for building the network includes: ground granulated blast furnace  slag (GGBS) as partial replacement for granite at 20, 40, 60, 80 and 100%; water-cement ratio (w/c) at  0.5, 0.55 and 0.6; curing age at 7, 14 and 28 days. Other factors, such as cement and sand were kept  constant. The input data were trained, learned and validated using the feed-forward back propagation  algorithm. From the various trial and errors performed, the optimal ANN model which yielded the  minimum mean square error and maximum absolute variance was 6-10-2. Therefore, based on the  high confidence level of the model predictions, the models are recommended for predicting strength  properties of steel slag aggregate concrete that falls within the limit of this study.