A Comparative Study of Machine Learning Methods for Prediction of Blast-Induced Ground Vibration | ||
| Journal of Mining and Environment | ||
| مقاله 5، دوره 12، شماره 3، مهر 2021، صفحه 667-677 اصل مقاله (1.33 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2021.11012.2077 | ||
| نویسندگان | ||
| A. Srivastava؛ B. Singh Choudhary؛ M. Sharma* | ||
| Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad, India | ||
| چکیده | ||
| Blast-induced ground vibration (PPV) evaluation for a safe blasting is a long-established criterion used mainly by the empirical equations. However, the empirical equations are again considering a limited information. Therefore, using Machine Learning (ML) tools [Support Vector Machine (SVM) and Random Forest (RF)] can help in this context, and the same is applied in this work. A total of 73 blasts are monitored and recorded in this work. For the ML tools, the dataset is divided into the 80-20 ratio for the training and testing purposes in order to evaluate the performance capacity of the models. The prediction accuracies by the SVM and RF models in predicting the PPV values are satisfactory (up to 9% accuracy). The results obtained show that the coefficient of determination (R2) for RF and SVM is 0.81 and 0.75, respectively. Compared to the existing linear regressions, this work recommends using a machine learning regression model for the PPV prediction. | ||
| کلیدواژهها | ||
| Empirical Equation؛ Ground Vibration؛ Peak Particle velocity؛ Random Forest Regression؛ Support Vector Regression | ||
| مراجع | ||
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