Abstract: Many construction companies and individuals (construction designers) are still using spreadsheets and laboratory tests just to obtain a certain data. In the field of technologies, advancement will contribute to the improvement of designing structures in terms of usefulness and effectiveness. By using the principle of artificial neural network, this study developed a sorptivity model which gives immediate quantities with high accuracy and precision which are needed to attain appropriate sorptivity values of concrete design mix. In this study, 40 concrete samples with varying percent replacement of copper slag to sand were tested for sorptivity by following the ASTM C1585 which is the Standard Test Method for Measurement of Rate of Absorption of Water by Hydraulic-Cement Concretes. These values in turn were used in the development of the sorptivity model using Artificial Neural Network. This study used the software called Matrix Laboratory (MATLAB) to train several neural networks. Several numbers of neurons in the hidden layer were considered because there is no actual study that suggests that a certain number of nodes in the hidden layer produce the best model. A parametric testing was conducted to determine which of the parameters considered have the greatest significance to the target output. The predicted results of the best model were compared to the experimental values of sorptivity and produced a 2.36 percentage error. The study results suggest that ANN models could be used to predict the sorptivity value of a concrete sample. The model produced a good prediction result.