Prediction and Optimization of Ground Vibration Caused by Blasts Using a Combination of Statistical Models and FROG Algorithm (Case Study: Gol-e-Gohar Iron Ore Mine No. 1) | ||
| Journal of Mining and Environment | ||
| مقاله 15، دوره 16، شماره 4، مهر و آبان 2025، صفحه 1403-1416 اصل مقاله (4.24 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2024.14747.2793 | ||
| نویسندگان | ||
| Abbas Khajouei Sirjani* 1؛ Farhang Sereshki1؛ Mohammad Ataei1؛ Mohammad Amiri Hossaini2 | ||
| 1Faculty of Mining, Petroleum & Geophysics Eng., Shahrood University of Technology, Shahrood, Iran | ||
| 2The Department of Mining and Geology of Research and Technology Management of Gol-e-Gohar, Sirjan, Iran | ||
| چکیده | ||
| The most significant detrimental consequence of blasting operations is ground vibration. This phenomenon not only causes instability in the mine walls but also extends its destructive effects to various facilities and structures over several kilometers. Various researchers have proposed equations for predicting Peak Particle Velocity (PPV), which are typically based on two parameters: the charge per delay and the distance to the blast site. However, according to different studies, the results of blasting operations are influenced by several factors, including the blast pattern, rock mass properties, and the type of explosives used. Since artificial intelligence technology has not yet been fully assessed in the mining industry, this study employs linear and nonlinear statistical models to estimate PPV at Golgohar Iron Ore Mine No. 1. To achieve this goal, 58 sets of blasting data were collected and analyzed, including parameters such as blast hole length, burden thickness, row spacing of the blast holes, stemming length, the number of blast holes, total explosive charge, the seismograph's distance from the blast site, and the PPV recorded by an explosive system using a detonating fuse. In the first stage, ground vibration was predicted using linear and nonlinear multivariate statistical models. In the second stage, to determine the objective function for optimizing the blast design using the shuffled frog-leaping algorithm, the performance of the statistical models was evaluated using R², RMSE, and MAPE indices. The multivariate linear statistical model, with R² = 0.9247, RMSE = 9.235, and MAPE = 12.525, was proposed and used as the objective function. Ultimately, the results showed that the combination of the statistical model technique with the shuffled frog-leaping algorithm could reduce PPV by up to 31%. | ||
| کلیدواژهها | ||
| Blasting؛ Ground Vibration؛ Statistical Models؛ Frog Algorithm؛ Gol-e-Gohar Mine | ||
| مراجع | ||
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