Blasting is currently the most important method for rock fragmentation in metal mines. However, blast-induced ground vibration causes many negative effects, including great damage to surrounding rock masses and projects and even casualties in severe cases. Therefore, prediction of the peak particle velocity (PPV) caused by blasting plays an important role in reducing safety threats. In this paper, a genetic algorithm (GA) and an artificial neural network (ANN) algorithm were jointly used to construct a neural network model with a 4-5-1 topology to predict the PPV. For this model, the ANN parameters were optimized using the GA, and the deviating direction, horizontal distance, vertical distance, Euclidean distance, explosive type, burden, hole spacing, and maximum charge per delay were used as input information. Moreover, principal component analysis (PCA) was used to extract the first four principal components from the eight input factors as the four inputs of the ANN model. The model was successfully applied to protect an underground crushing cave from blasting vibration damage by adjusting the blasting parameters. Compared with several widely used empirical equations, the GA-ANN PPV prediction model produced significantly better results, while the Ambraseys–Hedron method was the best of the empirical methods. Therefore, the improved GA-ANN model can be used to predict the PPV on site and provide a reference for the control of blasting vibration in field production.

中文翻译：

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使用改进的人工神经网络在阿舍勒铜矿进行爆破振动控制

爆破是目前金属矿山岩石破碎的最重要方法。然而，爆破引起的地面振动会产生许多负面影响，包括对围岩体和工程的巨大破坏，严重时甚至造成人员伤亡。因此，预测爆破引起的峰值粒子速度（PPV）对于减少安全威胁具有重要作用。在本文中，遗传算法（GA）和人工神经网络（ANN）算法联合使用构建具有4-5-1拓扑结构的神经网络模型来预测PPV。对于该模型，使用 GA 优化 ANN 参数，并将偏离方向、水平距离、垂直距离、欧氏距离、炸药类型、载荷、孔间距和最大每次延迟装药量用作输入信息。而且，主成分分析 (PCA) 用于从八个输入因素中提取前四个主成分作为 ANN 模型的四个输入。该模型通过调整爆破参数，成功应用于保护地下破碎洞免受爆破振动破坏。与几个广泛使用的经验方程相比，GA-ANN PPV 预测模型产生了明显更好的结果，而 Ambraseys-Hedron 方法是最好的经验方法。因此，改进后的GA-ANN模型可用于现场预测PPV，为现场生产爆破振动控制提供参考。该模型通过调整爆破参数，成功应用于保护地下破碎洞免受爆破振动破坏。与几个广泛使用的经验方程相比，GA-ANN PPV 预测模型产生了明显更好的结果，而 Ambraseys-Hedron 方法是最好的经验方法。因此，改进后的GA-ANN模型可用于现场预测PPV，为现场生产爆破振动控制提供参考。该模型通过调整爆破参数，成功应用于保护地下破碎洞免受爆破振动破坏。与几个广泛使用的经验方程相比，GA-ANN PPV 预测模型产生了明显更好的结果，而 Ambraseys-Hedron 方法是最好的经验方法。因此，改进后的GA-ANN模型可用于现场预测PPV，为现场生产爆破振动控制提供参考。