A new approach for estimation of rock brittleness based on non-destructive tests

This paper aims to propose predictive equations for estimation of rock brittleness as a function of intact rock properties including rock density (r), Schmidt hammer (Rn) and wave velocity (Vp) using two optimization techniques, artificial neural network (ANN) and FA-ANN (Firefly Algorithm and ANN). Using ANN and FA-ANN techniques, 10 different models were developed and compared to find the optimum one implementing some performance indices such as coefficient of determination (R-2) and root mean square error (RMSE). In addition, a ranking system was performed to select the best models. It was found that in developing ANN models, the Model number 1 is superior to other 4 models (models 2-5). Likewise, in developing hybrid FA-ANN technique, model number 9 was better than other 4 models (models 6-10). Further, the best models obtained with these two intelligent techniques were compared to show that hybrid model is better than a simple ANN model. It was found that R-2, RMSE, and total ranking are obtained as 0.826, 0.1481, and 19 for ANN while those are 0.896, 0.0812 and 36 for FA-ANN, respectively. It was also concluded that the model 9 of FA-ANN technique indicates the best performance among all developed hybrid models.