首页|基于镐形截齿侵入破岩试验的岩石可切割性分析与预测

基于镐形截齿侵入破岩试验的岩石可切割性分析与预测

Analyses and predictions of rock cuttabilities under different confining stresses and rock properties based on rock indentation tests by conical pick

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为了研究围压条件和岩石强度参数对岩石可切割性的影响,开展一系列镐形截齿侵入破岩试验.利用回归分析、支持向量机(SVM)和广义回归神经网络(GRNN)分析岩石可切割性与施加在岩石上的单轴围压和岩石强度参数(单轴抗压强度和抗拉强度)之间的关系.得到的回归模型和SVM模型可以准确反映岩石可切割性的变化规律.分析结果表明,随着单轴围压的增加,岩石的可切割性先降低后增加,岩石的可切割性与岩石抗压强度和抗拉强度呈负相关.根据预测模型计算得到镐型截齿切割坚硬磷矿石的最佳应力条件和切割参数,从而使基于多截齿旋转切割的纵轴悬臂式掘进机成功应用于坚硬磷矿石的开采.
The rock indentation tests by a conical pick were conducted to investigate the rock cuttability correlated to confining stress conditions and rock strength. Based on the test results, the regression analyses, support vector machine (SVM) and generalized regression neural network (GRNN) were used to find the relationship among rock cuttability, uniaxial confining stress applied to rock, uniaxial compressive strength (UCS) and tensile strength of rock material. It was found that the regression and SVM-based models can accurately reflect the variation law of rock cuttability, which presented decreases followed by increases with the increase in uniaxial confining stress and the negative correlation to UCS and tensile strength of rock material. Based on prediction models for revealing the optimal stress condition and determining the cutting parameters, the axial boom roadheader with many conical picks mounted was satisfactorily utilized to perform rock cutting in hard phosphate rock around pillar.

rock cuttabilityrock indentationprediction modelregression analysissupport vector machineneural network

王少锋、唐宇、李夕兵、杜坤

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中南大学 资源与安全工程学院,长沙 410083

中南大学 高等研究中心,长沙 410083

岩石可切割性 侵入破岩 预测模型 回归分析 支持向量机 神经网络

authors are grateful for the financial supports from the National Natural Science Foundation of Chinaauthors are grateful for the financial supports from the National Natural Science Foundation of China

5190433351774326

2021

10.1016/S1003-6326(21)65615-7

中国有色金属学报(英文版)
中国有色金属学会

中国有色金属学报(英文版)

CSTPCDCSCDSCI
影响因子：1.183
ISSN：1003-6326
年,卷(期)：2021.31(6)
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