Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics

Before any rock engineering project, mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests, but in some situations preparing the required specimens is impossible. By this time, several models have been estab- lished to evaluate UCS and E from rock substantial properties. Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this tech- nique compared to classic statistical techniques. In this paper, ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models, they were compared with laboratory results. For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void, mineral content and grain size as well as UCS were determined. To select the best predictors as inputs of the UCS models, this data set was subjected to statistical analyses comprising basic descrip- tive statistics, bivariate correlation, curve fitting and principal component analyses. Results of such anal- yses have shown that void, ferroan calcitic cement, argillaceous cement and mica percentage have the most effect on USC. Two predictive models for UCS were developed using these variables by ANN and lin- ear multivariate regression. Results have shown that by using simple textural characteristics such as min- eral content, cement type and void, strength of studied sandstone can be estimated with acceptable accuracy. ANN and multivariate statistical UCS models, revealed responses with 0.87 and 0.76 regres- sions, respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.