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Blast Vibration Analysis by Different Predictor Approaches-A Comparison

机译:不同预测仪方法的爆炸振动分析 - 比较

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Blasting is a major mechanism of rock fragmentation. Explosive usage creates ground vibration, air over pressure as well as fly rock. Blast design is a process which needs to be monitored continuously as the rock mass exhibit many vibrations within the same boundary. Often the quantum of explosive had to be determined within safe vibration limit. Artificial Neural Network approach is also gaining wide recognition for its accuracy to match with the measured values. This paper evaluates the governing relations between PPV at varying explosive quantities and distances using established approaches as well as the results by application of artificial neural network. A total of nine blast events have been analyzed. Indian Standard and ANN approaches exhibit better correlation with the measured values of PPV as compared to that by other approaches.
机译:爆破是岩石碎片的主要机制。爆炸用法创造地面振动,空气过压以及飞岩。 BLAST设计是一种过程,需要连续监测,因为岩体在同一边界内表现出许多振动。通常必须在安全的振动极限内确定炸药量。人工神经网络方法也获得了广泛的识别,以便其准确性与测量值匹配。本文评估了PPV在不同爆炸量和使用建立方法的距离之间的控制关系以及应用人工神经网络的结果。共分析了九次爆炸事件。与其他方法相比,印度标准和ANN方法与PPV的测量值表现出更好的相关性。

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