Prediction of compressive strength of concrete with rice husk ash (RHA) as partial replacement for cement using artificial neural network (ANN) model

Abstract

Rice husk Ash (RHA) is a bye-product of rice paddy milling industries and due to their rough surface and abrasive nature, they have less tendency for natural degradation and pose serious disposal problems. Many research works are ongoing to study the feasibility of using RHA in a better way as a cementitious material and for the partial replacements for cement content in concrete. In the current research work, Artificial Neural Network (ANN) method is used for the prediction of compressive strength of concrete with RHA as a cementitious material. The experimental results of the author and the results taken from various published literature are used in developing an analytical model using ANN for the prediction of compressive strength of concrete with RHA. Experimental works were carried out to find the cube compressive strength of concrete with 5 different % of RHA content as the cementitious material at various ages (3 days, 7days, 28 days and 56 days) of concrete. The ANN model is developed as a function of seven variables such as cement content(C), sand content(S), coarse aggregates (CA), rice husk ash (RHA), water(W), super plasticizer content (SP) and the age of concrete (Age) testing. Three different networks (Levenberg–Marquardt, Bayesian regularization and Scaled conjugate gradient) available for ANN in MAT LAB tool were constructed, trained and tested using the experimental data available in various research works covering a large range of concrete compressive strengths at various ages. The experimental compressive strength of concrete (with RHA) mixes was compared with the predicted values using ANN model and it is found that the ANN model assesses the compressive strength with higher accuracy.