ANN model of brick properties using LPNORM calculation of minerals content

The influence of minerals quantity, along with the firing temperature (800-1100 °C), and several shape formats of laboratory brick samples were investigated, and the acquired data were used to build Artificial Neural Network (ANN) model. ANN model was developed in order to predict the final products parameters, and its results have been afterwards compared to experimental data. ANN model, coupled with sensitivity analysis, was obtained with high prediction accuracy, according to coefficient of determination, r2: 0.880-0.884 in compressive strength calculation, 0.954-0.960 for water absorption, 0.869 for firing shrinkage, 0.979-0.984 for water loss during firing and 0.907 for volume mass of cubes model.