Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness

This paper presents the application of a neural network for the prediction of the UCS from hardness tests on rock samples. To investigate the suitability of this approach, the results of the network are compared to predictions obtained by conventional statistical relations. The network was trained to predict the UCS based on the hardness, porosity, density, grain size and ruck type information of a rock sample. A dataset containing 194 rock sample records, ranging from weak sandstones to very strong granodiorites, was used to train the network with the Levenberg-Marquardt training algorithm. Two sets, each containing 17 rock samples, were used to validate the generalization and prediction capabilities of the network. (C) 1999 Elsevier Science Ltd. All rights reserved.