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Abstract

Excavation in open-pit mines generally comprises the method of drilling and blasting with regard to economic efficiency and effectively supporting large-scale operations. However, it develops ground vibrations that may affect nearby structures and localities. Its accurate prediction along with mitigation strategies is essential. Most often, empirical relations are used in predicting vibration, the present study investigates the potential application of machine learning (ML) methods in improving predictions. Machine learning algorithms, including Linear Regression (LR), Decision Trees (DT), Random Forests (RF), and Support Vector Machines (SVM), were applied to forecast Peak Particle Velocity (PPV) and compared against observed ground vibration data. The analysis utilized datasets from the AlvandQoly limestone mine (177 observations) and the Seyitomer lignite mine (76 observations), representing diverse blasting conditions. In Case Study 1, the R²/RMSE values are LR (1.00/3.71e-16), DT (0.99/0.0139), RF (0.99/0.0074), and SVM (0.93/0.0543), it is observed that the fit of the LR model is highly reliable. For Case Study 2, the R²/RMSE values are LR (0.83/0.0683), DT (0.88/0.0561), RF (0.88/0.0560), SVM (0.77/0.0783), and the best performance was shown by the RF model in Case Study 2, hence making it the most robust and balanced for changing geological conditions. Therefore, a comparative analysis across all the models shows that the ensembling methods, such as RF, were consistently performing better compared to other algorithms, especially on more complex data distributions. The test results prove that machine learning techniques can replace or support the empirical methods of blast vibration forecasting with more accuracy, flexibility, and ease in application. Therefore, this development offers a dependable framework for the mitigation of the environmental and structural effects of blasting operations at open-pit mining, contributing to safer and more sustainable practices.

Keywords

blast-induced ground vibration (BIGV)peak particle velocity (PPV)scaled distance (SD)decision tree (DT)random forests (RF)support vector machine (SVM)

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Machine learning-driven framework for blast vibration assessment in mining sites

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