Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation. Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9 m, spacing of 4.4 m and charge per delay of 627 Kg.

中文翻译：

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优化露天石灰石矿山的爆破设计，以使用可靠的技术减少地面振动

爆破作业给位于矿井附近的住宅和其他建筑物造成了严重的问题。爆破振动是爆破的最关键因素之一，应准确估算出爆破振动的影响。在本文中，已经尝试将各种模型应用于预测由于爆破引起的地面振动。为了实现这一目标，在伊朗的一个石灰岩矿山中精确测量并收集了112次爆炸操作。这些爆炸操作数据被用于构建人工神经网络（ANN）模型以预测峰值粒子速度（PPV）。在这项研究中使用的输入参数是负荷，间距，每次延迟的最大装料量，从爆破面到监测点的距离以及岩石质量的指定，输出参数为PPV。对相同的数据集还进行了常规的经验预测指标和多元回归分析，以研究PPV。因此，观察到与其他采用的预测变量相比，ANN模型更为准确。此外，还表明，对地面振动影响最大的参数是距爆炸的距离和每次延迟的最大装药量，而最无效的参数是负担，间距和岩石质量指定。最后，为了最小化PPV，将开发的ANN模型用作帝国主义竞争算法（ICA）的目标函数。最终，通过考虑2.9 m的负担，4.4 m的间距和627 Kg的每次延迟电荷，发现ICA算法能够将PPV降低多达59％。