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<p>Fragmentation is a key indicator for evaluating blasting quality, but accurate prediction of post-blast rock fragmentation remains challenging due to the complexity of blasting parameters and rock properties. This study theoretically reveals the formation mechanism of rock fragmentation during blasting and proposes an energy fan-based fragmentation prediction model (Par-12), incorporating variables such as explosive performance, rock basic quality index (<i>BQ</i>), and surface energy (<i>γ</i>). The model parameters were calibrated via nonlinear fitting using 109 bench blast datasets collected from various locations, rock types, bench geometries, and delay times. The reliability of the proposed prediction model was validated using measured fragmentation data from blasting experiments conducted at the Changjiu Shenshan limestone mine. A comparison of this model with the xP-EF, xP-frag, and Kuz-Ram models reveals that the median absolute relative error (MARE<sub>median</sub>) of the present model for full-scale fragmentation distribution is 19.88%, significantly lower than xP-frag (28.52%), Kuz-Ram (38.92%), and xP-EF (24.36%), demonstrating that the newly developed Par-12 model possesses excellent predictive capability for rock blast fragmentation.</p>

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Blast Fragmentation Prediction Model Based on Energy Fan Theory

  • 期刊论文

  • 发表日期：

    2025年11月6日

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  • Dongqiang Liu,
  • Xinhua Zeng,
  • Ming Chen,
  • Peng Zhang,
  • Jiangping Yan,
  • Wenbo Lu,
  • Fengze Zhao,
  • Mingze Li,
  • Zhanzhi Tan

摘要

Fragmentation is a key indicator for evaluating blasting quality, but accurate prediction of post-blast rock fragmentation remains challenging due to the complexity of blasting parameters and rock properties. This study theoretically reveals the formation mechanism of rock fragmentation during blasting and proposes an energy fan-based fragmentation prediction model (Par-12), incorporating variables such as explosive performance, rock basic quality index (BQ), and surface energy (γ). The model parameters were calibrated via nonlinear fitting using 109 bench blast datasets collected from various locations, rock types, bench geometries, and delay times. The reliability of the proposed prediction model was validated using measured fragmentation data from blasting experiments conducted at the Changjiu Shenshan limestone mine. A comparison of this model with the xP-EF, xP-frag, and Kuz-Ram models reveals that the median absolute relative error (MAREmedian) of the present model for full-scale fragmentation distribution is 19.88%, significantly lower than xP-frag (28.52%), Kuz-Ram (38.92%), and xP-EF (24.36%), demonstrating that the newly developed Par-12 model possesses excellent predictive capability for rock blast fragmentation.