Abstract
The Uniaxial Compressive Strength (UCS) of intact rocks is an essential index of strength in rock engineering. Laboratory based
direct compressive strength estimation may be problematic, as obtaining fresh samples is not always feasible. Thus, the aim of
indirect methods index test such as point load index test, and empirical correlations with UCS of indexes like the Brazilian
indirect tensile strength test, serve as an alternative for many geotechnical engineering projects. The aim of this paper is to
propose a relationship between UCS and indirect tests or indexes for some sedimentary and igneous rocks in KwaZulu-Natal
using the technology of artificial intelligence. These tests include the point load index (Is (50)) test and Brazilian Tensile
Strength (σt), test. Block samples were collected in KwaZulu Natal, among these include sedimentary rocks (sandstones,
siltstone, tillite) and igneous rocks (granitoids and dolerite). A back propagation artificial neural network was developed and
trained in order to predict UCS. The input parameters were unit weight γ, (Is (50)), (σt), and lithology. The lithology was
introduced in the neural network as a qualitative input parameter, in order to indirectly incorporate in the model the mineralogical
content. Training results returned, R value of 0.99% for the training set, and R = 0.92% for the test set, which is conveying to
the conclusion that the approach is valid and could be used, as an alternative indirect approach to UCS estimation.