AN ANN MODEL FOR PREDICTING SLUMP OF CONCRETE CONTAINING CRUSHED GLASS AND NATURAL GRAVEL

Abstract

This study modelled the slump of concrete containing crushed glass and Bida Natural Gravel (BNG) based on deep learning algorithm using the MATLAB neural network toolbox. A total of 240 (150mm × 150mm × 150mm) cubes were cast from 80 mixes generated randomly using Scheffe’s simplex lattice approach. Slump was measured for each of the experimental points of fresh concrete before filling in the moulds. The resulting batch for each mix was used as input data while the laboratory results for slump was used as output data for the ANN-model. Hence a shallow multilayer supervised Neural Network was developed to model these data. The developed model would be able to predict concrete slump containing 0% - 25% crushed glass as partial replacement for fine aggregate, water- cement ratio ranging from 0.45 – 0.65 and concrete grade M15 – M25. The architecture of the network contained 6 input parameters: water to cement ratio, water, cement, sand, crushed glass and BNG, 20 neurons in the hidden layer and slump in the outer layer. The adequacy of the developed model was measured using Mean Square Error (MSE) and Correlation Coefficient (R). Results showed that 6:20:1 model architecture for slump model had an MSE values for training, validation and testing as: 1.84e-2, 5.81e-3, 3.64e-3, 1.73e-3 respectively. Regression values for training, validation and testing are: 79%, 94%, 96% and 79%. The study concluded that a shallow multilayer Neural Network architecture with 20 neurons in the hidden layer is sufficient for predicting concrete slump.