Assessing Blasting-Induced Noise in Open-Pit Mining: A Comparative Approach Using Analytical Models and Machine Learning | ||
| Journal of Mining and Environment | ||
| مقاله 7، دوره 17، شماره 3، مرداد و شهریور 2026، صفحه 875-893 اصل مقاله (12.11 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2025.16675.3271 | ||
| نویسندگان | ||
| Zaenal Zaenal1؛ Noor Fauzi Isniarno1؛ Delina Mutiara1؛ Sofie Nur’aini2؛ Hasyim Fadhilah2؛ Elfida Moralista2؛ Andrieanto Nurrochman* 2 | ||
| 1Department of Mining Engineering, Faculty of Engineering, Universitas Islam Bandung, Bandung, Indonesia | ||
| 2Department of Mining Engineering, Faculty of Engineering, Universitas Islam Bandung, Bandung Indonesia | ||
| چکیده | ||
| Blasting is a fundamental open-pit mining operation necessary for rock breakage, but it also generates significant environmental noise pollution. Excessive noise from blasting not only endangers health but also poses problems to compliance with regulations, particularly in regions where acoustic standards differ, such as Indonesia's use of both dBL and dBA standards. This research addresses the need for reliable and context-dependent predictive models for blasting noise, aiming to compare analytical and empirical formulas with machine learning techniques in dBA prediction. Measurements were conducted at 30 blasts at an open-pit coal mine in Indonesia, South Sumatra, using homogeneous acoustic sensors. The measured data points for frequency, dBL, and dBA were matched to calculated data using equations. Random Forest (RF) and Artificial Neural Network (ANN) predictive models using measured frequency and dBL as predictive variables were also derived. Results show that used Finn-derived equation has poor predictive accuracy, with errors exceeding 80%. Among the analytical and empirical models, Equation 3 performed the best, with an average error of 9%, while a site-spesific regression model based on measurements had an improved error rate of 5%. Machine learning models outperformed all models, with the RF model exhibiting an average error of 2% and demonstrating higher stability and consistency. The ANN model also did well, but with more variation and some overestimations. | ||
| کلیدواژهها | ||
| Blasting noise؛ Machine learning؛ Environmental impact؛ Noise prediction models | ||
| مراجع | ||
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