Prediction of blast‑induced ground vibrations in quarry sites: a comparison of GP, RSM and MARS

Among the side effects caused by the blast, ground vibration (GV) is the most important one and can make serious damages to the surrounding structures. According to many scholars, the peak particle velocity (PPV) is one of the main indicators for determining the extent of blast‑induced GVs. Recently, following the rapid growth of soft computing approaches, researchers have tried to use these new techniques. This paper aims to explore three methods of soft computing including genetic programming (GP), response surface methodology (RSM), and multivariate adaptive regression splines (MARS) to predict the PPV values. For this purpose, a dataset of 200 published data including PPV, distance from the blasting face (D), and charge weight per delay (W) was used. The data have been recorded using blast seismograph, during the blast-induced earthquake triggered at 10 quarry sites in Ibadan and Abeokuta areas, Nigeria (https://doi.org/10.1016/j.dib.2018.04.103). The coefficient of determination for the MARS model as a most accurate model built in this research based on overall data results (R2 = 0.81), compared with the most accurate empirical equations presented in the research literature, namely general predictor model (R2 = 0.78), had a variation equal to 0.02. This variation for the root mean of squared error (RMSE), mean of absolute deviation (MAD), and mean of absolute percent error (MAPE) values were equal to 0.85, 0.25, and 0.38, respectively. In addition, the sensitivity analysis using cosine amplitude method (CAM) showed that the influence of each D and W parameters on PPV values based on developed models by this paper was more similar with the influence of these parameters based on the actual values, compared to empirical models. Finally, the parametric studies to investigate the behavior of various developed models were done to survey the changes to the values of the two variables D and W. •GP, RSM, and MARS methods were employed to predict peak particle velocity (PPV) values.•A dataset of 200 published data was used (https://doi.org/10.1016/j.dib.2018.04.103).•AI-based models showed better performance compared to empirical equations.•The sensitivity analysis showed the influence of each D and W parameters on PPV values based on developed models.•The parametric studies to investigate the behavior of various developed models were done.