The uniaxial compressive strength (UCS) and Young’s modulus (E) are important parameters in designing solutions to rock engineering problems. However, determination of these properties in the laboratory is expensive and time consuming. Therefore, many attempts have been made to estimate these properties indirectly by defining various correlations. These correlations often relate UCS and E to some basic rock index tests. Nevertheless, in this study, using an artificial neural network (ANN) enhanced with the imperialist competitive algorithm (ICA), a hybrid model is developed for predicting the UCS and E of granite samples. The samples used in this study were taken from the face of the Pahang– Selangor raw water transfer tunnel in Malaysia. To train the aforementioned model, the results of the laboratory tests, including porosity (n), P wave velocity (VP), point load strength index (Is(50)) and the Schmidt hammer rebound number (Rn),were used as model inputs. For the sake of comparison, the performance of the hybrid model was checked against a conventional ANN predictive model with similar architecture. Value account for (VAF), root mean square error (RMSE) and coefficient of determination (R2) were used to control the capacity performance of the predictive models. The performance indices obtained using the ICA-ANN approach show that the proposed model can predict UCS and E with a high degree of accuracy. The results of sensitivity analysis reveal that VP is the most influential parameter, compared to the other input parameters, on UCS and E.
کلید واژگان :UCS . Young’smodulus . ICA .ANN . Granite
ارزش ریالی : 600000 ریال
با پرداخت الکترونیک