Blasting is the predominant rock fragmentation technique in civil constructions, underground and surface mines. Flyrock is the unwanted throw of rock fragments during blasting and is the major cause of considerable damage in and around the mines. The present research aimed to propose a new intelligence-based method to predict flyrock. In this regard, the recurrent fuzzy neural network (RFNN) combined with the genetic algorithm (GA) is proposed. For checking the suitability of the RFNN-GA model, artificial neural network (ANN), hybrid ANN and GA and a nonlinear regression model were also employed. To achieve the aims of the research, data for 70 blasting sites including four input parameters (spacing, burden, stemming and maximum charge per delay) and one output parameter (flyrock) were gathered from two quarry mines at the Shur River dam, Iran. The performance of the proposed prediction methods was then assessed with statistical evaluation criteria, i.e., R-square and root mean square error. The results indicate the proposed RFNN-GA model was more superior for prediction of flyrock than the GA-ANN, ANN and nonlinear regression models. According to a sensitivity analysis, the maximum charge per delay was the most influential parameter in flyrock prediction in this case.

中文翻译：

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矿山爆破中的飞石预测：一种新的计算智能方法

爆破是民用建筑，地下和露天矿山中主要的岩石破碎技术。飞石是爆破过程中不必要的碎石投掷，并且是矿井内和矿井附近造成重大破坏的主要原因。本研究旨在提出一种新的基于情报的方法来预测飞石。为此，提出了结合遗传算法（GA）的递归模糊神经网络（RFNN）。为了检查RFNN-GA模型的适用性，还使用了人工神经网络（ANN），混合ANN和GA以及非线性回归模型。为了实现研究的目的，从伊朗舒尔河大坝的两个采石场收集了70个爆破现场的数据，包括四个输入参数（间距，负担，阻止和最大每次延迟装料）和一个输出参数（飞石）。R平方和均方根误差。结果表明，所提出的RFNN-GA模型比GA-ANN，ANN和非线性回归模型对飞石的预测更为出色。根据敏感性分析，在这种情况下，每次延迟的最大电荷是飞石预测中影响最大的参数。