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The accurate determination of strength parameters of rocks such as uniaxial compressive strength (UCS) and elastic modulus (E) using direct and laboratory methods require substantial time and cost. Therefore, the production of predictive relationships and models to forecast the UCS and E is of critical necessity in rock engineering. This study deals with the estimation of UCS and E of sandstones from petrographic characteristics by an artificial neural network (ANN) and multiple regression. For this purpose, 130 core specimens were prepared from sandstones in different locations in Iran. The specimens were tested to determine UCS, E, dry density, and porosity. Also, the petrographic studies including the determination of 11 textural and mineralogy parameters were performed on selected samples. The performance of the ANN model and regression analysis was evaluated using the criteria such as correlation coefficient (R), root mean squared error (RMSE), and variance account for (VAF). According to the ANN results, values of R, RMSE, and VAF were obtained to be 0.925, 0.089, and 97% for UCS and 0.876, 0.094, and 96% for E, respectively. In comparison, for the MLR model, the obtained R, RMSE, and VAF were 0.845, 0.101, and 95% for UCS and 0.797, 0.116, and 93% for E, respectively. A comparison between the findings illustrated that the ANN model was more suitable for forecasting the UCS and E compared with the MLR method.

Estimation Of Mechanical Properties Of Sandstones From Petrographic Characteristics Using Artificial Neural Networks (ANNs)