This work focuses on development of Artificial Neural Networks (ANNs) in prediction of compressive strength of reactive powder concrete after 28 days. To predict the compressive strength of reactive powder concrete nine input parameters that are cement, water, silica fume, fly ash, Ground granulated blast Furnace slag, super plasticizer, fine aggregate, Quartz sand and steel fibres are identified. A total of 35 different data sets of concrete were collected from the technical literatures. Number of layers, number of neurons, activation functions were considered and the results were validated using an independent validation data set. A detailed study was carried out, considering single hidden layers for the architecture of neural network. The performance of the 9-3-1 architecture was the best possible architecture. The results of the present investigation indicate that ANNs have strong potential as a feasible tool for predicting the compressive strength of reactive powder concrete.
