Exploitation
Abbas Khajouei Sirjani; Farhang Sereshki; Mohammad Ataei; Mohammad Amiri Hossaini
Abstract
Rock fragmentation induced by blasting plays a critical role in the productivity and cost efficiency of open-pit mining operations. Among blast design strategies, air-deck blasting has been proposed as a technique to improve energy utilization by modifying the pressure–time characteristics within ...
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Rock fragmentation induced by blasting plays a critical role in the productivity and cost efficiency of open-pit mining operations. Among blast design strategies, air-deck blasting has been proposed as a technique to improve energy utilization by modifying the pressure–time characteristics within the blasthole. However, its performance under production-scale conditions and the reliability of numerical predictions remain insufficiently validated. This study presents an integrated field–numerical investigation of air-deck blasting at the Gol-e-Gohar Iron Ore Mine No. 1, Iran. Fragmentation characteristics were quantified using image-based particle size distribution (PSD) analysis of muck piles processed with Split-Desktop software. Characteristic fragmentation indices (D20, D50, and D80) were extracted to evaluate blast performance. Three-dimensional numerical simulations were performed using the LS-DYNA explicit finite element code to model stress-wave propagation, damage evolution, and fragmentation development for both conventional blasting and air-deck blasting configurations. Numerical models were calibrated using site-specific blasting geometry, explosive properties, and rock mass parameters derived from field measurements. The results show strong agreement between numerical predictions and field observations, with coefficients of determination exceeding 0.95 and RMSE values below 10%. Compared with conventional blasting, air-deck blasting produced finer and more uniform fragmentation, reducing D50 by approximately 10–15% and D80 by up to 18%. The improvement is primarily attributed to stress-wave reflection at the air gap and enhanced tensile crack propagation. The proposed field-validated numerical framework provides a practical tool for blast design optimization and demonstrates the potential of air-deck blasting to improve fragmentation efficiency in large-scale open-pit mining operations.
Exploitation
Abbas Khajouei Sirjani; Farhang Sereshki; Mohammad Ataei; Mohammad Amiri Hossaini
Abstract
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 ...
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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%.
