  Conventionally, many researchers have used both regression and black box
techniques to estimate the unconfined compressive strength (UCS) of different
rocks. The advantage of the regression approach is that it can be used to
render a functional relationship between the predictive rock indices and its
UCS. The advantage of the black box techniques is in rendering more accurate
predictions. Gene expression programming (GEP) is proposed, in this study, as a
robust mathematical alternative for predicting the UCS of carbonate rocks. The
two parameters of total porosity and P-wave speed were selected as predictive
indices. The proposed GEP model had the advantage of the both traditionally
used approaches by proposing a mathematical model, similar to a regression,
while keeping the prediction errors as low as the black box methods. The GEP
outperformed both artificial neural networks and support vector machines in
terms of yielding more accurate estimates of UCS. Both the porosity and the
P-wave velocity were sufficient predictive indices for estimating the UCS of
the carbonate rocks in this study. Nearly, 95% of the observed variation in the
UCS values was explained by these two parameters (i.e., R2 =95%).
