RMR(Rock Mass Rating)是国际上广泛应用的岩体质量分级系统,它包含5项基本指标,即岩石单轴抗压强度(Rc)、岩石质量指标RQD、节理间距(D)、节理条件(Jcond)及地下水条件(GW)。其中岩石质量指标和节理间距均用来反映岩体的节理化程度,两...RMR(Rock Mass Rating)是国际上广泛应用的岩体质量分级系统,它包含5项基本指标,即岩石单轴抗压强度(Rc)、岩石质量指标RQD、节理间距(D)、节理条件(Jcond)及地下水条件(GW)。其中岩石质量指标和节理间距均用来反映岩体的节理化程度,两者之间存在一定的相关关系,故在岩体分级系统中存在重复使用的问题,并且在实际应用中,岩石质量指标和节理间距的获取往往存在一定的困难,且具有一定的方向性和不确定性。采用岩体完整性指数代替原分级方案中的岩石质量指标及节理间距,建立了一种改进的RMR分级体系,从而省去了对这两项指标的野外测量和计算,提高了工作效率。该方法在玛尔挡工程中进行实例验证,与原RMR方法及国标BQ的分级对比结果表明:利用这一改进的分级方法获得的结果介于RMR与BQ之间,是一种切实可行的分级方案,可在更多的工程中应用。展开更多
The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boul...The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively.展开更多
文摘RMR(Rock Mass Rating)是国际上广泛应用的岩体质量分级系统,它包含5项基本指标,即岩石单轴抗压强度(Rc)、岩石质量指标RQD、节理间距(D)、节理条件(Jcond)及地下水条件(GW)。其中岩石质量指标和节理间距均用来反映岩体的节理化程度,两者之间存在一定的相关关系,故在岩体分级系统中存在重复使用的问题,并且在实际应用中,岩石质量指标和节理间距的获取往往存在一定的困难,且具有一定的方向性和不确定性。采用岩体完整性指数代替原分级方案中的岩石质量指标及节理间距,建立了一种改进的RMR分级体系,从而省去了对这两项指标的野外测量和计算,提高了工作效率。该方法在玛尔挡工程中进行实例验证,与原RMR方法及国标BQ的分级对比结果表明:利用这一改进的分级方法获得的结果介于RMR与BQ之间,是一种切实可行的分级方案,可在更多的工程中应用。
文摘The most important objective of blasting in open pit mines is rock fragmentation.Prediction of produced boulders(oversized crushed rocks) is a key parameter in designing blast patterns.In this study,the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine,Iran was predicted via multiple regression method and artificial neural networks.Results of 33 blasts in the mine were collected for modeling.Input variables were:joints spacing,density and uniaxial compressive strength of the intact rock,burden,spacing,stemming,bench height to burden ratio,and specific charge.The dependent variable was ratio of boulder volume to pattern volume.Both techniques were successful in predicting the ratio.In this study,the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19,respectively.
