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首页> 外文期刊>Journal Of The South African Institute Of Mining & Metallurgy >The prediction of penetration rate for percussive drills from indirect tests using artificial neural networks
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The prediction of penetration rate for percussive drills from indirect tests using artificial neural networks

机译：采用人工神经网络从间接试验预测冲击钻头的渗透率

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摘要

Percussive drills are widely used in engineering projects such as mining and construction. The prediction of penetration rates of drills by indirect methods is particularly useful for feasibility studies. In this investigation, the predictability of penetration rate for percussive drills from indirect tests such as Shore hardness, P-wave velocity, density, and quartz content was investigated using firstly multiple regression analysis, then by artificial neural networks (ANNs). Operational pressure and feed pressure were also used in the analyses as independent variables. ANN analysis produced very good models for the prediction of penetration rate. The comparison of ANN models with the regression models indicates that ANN models are the more reliable. It is concluded that penetration rate for percussive drills can be reliably estimated from the Shore hardness and density using ANN analysis.
机译：打击性钻头广泛用于工程项目，如采矿和施工。 间接方法预测钻头的渗透率对于可行性研究特别有用。 在该研究中，使用首先多元回归分析研究了从间接测试的间接试验（如肖氏硬度，P波速度，密度和石英含量）的急性试验的渗透率的可预测性。 作为独立变量的分析中也使用操作压力和进料压力。 Ann分析为预测渗透率产生了非常好的模型。 随着回归模型的ANN模型的比较表明ANN模型更可靠。 得出结论是，使用ANN分析，可以从肖氏硬度和密度可靠地估计冲击钻头的渗透率。

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