Blast-induced flyrock is one of the most dangerous events that can be harmful to the mine personnel and equipment. Therefore, accurate prediction of this phenomenon would be essential for blasting operations. In this paper, a mathematical model was developed to predict flyrock using a statistical method. In the first step, linear multivariate regression (LMR) was used to establish the mathematical flyrock model, and then in the second step, gene expression programming (GEP) was employed to enhance statistical model appropriateness. Input parameters were considered to be burden, hole spacing, length of stemming, and powder factor, while output parameter was set to be flyrock. The required data were collected from Topal Novin limestone mine, Iran. According to the obtained results, it was observed that the performance of the developed GEP predictor model is much better than that of the LMR model. For efficiency comparison of the presented models, R square and RMSE were computed 0.86 and 13.26 and 0.91 for LMR and 10.81 for GEP, which shows superiority of the GEP technique over LMR method.

爆破引发的飞石是最危险的事件之一，可能对矿场人员和设备造成伤害。因此，准确预测这种现象对于爆破作业至关重要。在本文中，开发了一个数学模型来使用统计方法预测飞岩。第一步，使用线性多元回归（LMR）建立数学飞石模型，然后在第二步，使用基因表达编程（GEP）来增强统计模型的适用性。输入参数被认为是炉料、孔距、堵塞长度和粉末系数，而输出参数被设置为飞石。所需数据来自伊朗 Topal Novin 石灰石矿。根据得到的结果，据观察，开发的 GEP 预测器模型的性能比 LMR 模型好得多。对于所提出模型的效率比较，LMR 的 R 平方和 RMSE 计算为 0.86、13.26 和 0.91，GEP 计算为 10.81，这表明 GEP 技术优于 LMR 方法。