Brittleness of rock is one of the most critical features for design of underground excavation project. Therefore, proper assessing of rock brittleness can be very useful for designers and evaluators of geotechnical applications. In this study, feasibility of genetic programming (GP) model and non-linear multiple regression (NLMR) in predicting brittleness of intact rocks is examined. For this purpose, a dataset developed by conducting various rock tests including uniaxial compressive strength, Brazilian tensile strength, unit weight and brittleness via punch penetration on rock samples gathered from 48 tunnels projects around the world is utilized herein. Considering multiple inputs, several GP models were constructed to estimate brittleness index of the rock and finally, the best GP model was selected. Note that, GP can make an equation for predicting output of the system using model inputs. To show applicability of the developed GP model, non-linear multiple regression (NLMR) was also applied and developed. Considering some model performance indices, performance prediction of the GP and NLMR models were evaluated and it was found that the GP model is superior to NLMR one. Based on coefficient of determination (R (2)) of testing datasets, by proposing GP model, it can be improved from 0.882 (obtained by NLMR model) to 0.904. It is worth mentioning that the proposed predictive models in this study should be planned and used for the similar types of rock and the established inputs ranges.
Blasting is a widely used technique for rock fragmentation in surface mines and tunneling projects. The ground vibrations produced by blasting operations are the main concern for the industries undertaking blasting operations, which can damage the surrounding structures, adjacent rock masses, roads and slopes in the vicinity. Therefore, proper prediction of blast-induced ground vibrations is essential to demarcate the safety area of blasting. In this research, classification and regression tree (CART) as a rule-based method was used to predict the peak particle velocity through a database comprising of 51 datasets with results of maximum charge per delay and distance from the blast face were fixed as model inputs. For comparison, the empirical and multiple regression (MR) models were also applied and proposed for peak particle velocity prediction. Performance of the proposed models were compared and evaluated using three statistical criteria, namely coefficient of correlation (R (2)), root mean square error (RMSE) and variance account for (VAF). Comparison of the obtained results demonstrated that the CART technique is more reliable for predicting the peak particle velocity than the MR and empirical models and it can be introduced as a new technique in this field.