Recurrent neural network-based prediction of compressive and flexural strength of steel slag mixed concrete

Abstract

In this paper, an effort has been made to develop a recurrent type of neural network known as diagonal recurrent neural network (DRNN) to predict the compressive and flexural strengths of AOD steel slag-mixed concrete for pavements. The data used for modeling were attained from the laboratory experiments. The compressive and flexural strengths were experimentally analyzed for specimens containing 0%, 10%, 15%, 20%, and 25% of AOD steel slag as a partial replacement of cement at curing ages of 3, 7, 28, 90, 180, and 365 days. The developed model was trained using the backpropagation (BP) algorithm. The performance of the proposed model during the training and validation has been compared with the well-known prediction models such as multi-layer perceptron (MLP) and the radial basis function network (RBFN). The DRNN-based prediction model has given much better prediction results when compared to the other two models since the former provided comparatively smaller values of performance indicators such as average mean square error (AMSE) and mean average error (MAE). The reason for DRNN performing better than the other two models is that it contains feedback connections/weights which induce memory property in its structure. This helps DRNN to better model the complex mappings. Such feedback loops are not available in MLP and RBFN. The study conducted in this research concludes that the DRNN-based prediction model should be preferred over the MLP and RBFN models for predicting the compressive and flexural strengths of AOD steel slag added to concrete for pavements.