【摘要】 骨料堆积密实度不仅影响混凝土和充填体的强度,还关系到新拌混凝土或充填料浆的和易性与管输性,因此它是混凝土工程和充填矿山研究课题之一。本实验在明确基本概念的基础上,首先探讨了五个堆积率计算模型的特性。然后分析了三个颗粒间相互作用影响堆积率的壁效应及松动效应模型,讨论其差异性及适用范围。针对金川镍矿废石骨料进行堆积密实度实验和计算模型验证,结果表明,不同粗细颗粒混合比下所适用的模型也不同,现有堆积模型难以精确预测实际堆积率。最后提出基于径向基函数(RBF)神经网络黑盒建模思路及实现过程,建立能准确预测骨料堆积率计算的通用模型,并对模型特点进行了讨论。更多还原

【Abstract】 Packing density influences the flowability,segregation resistance and the strength of concrete or filling bodies,thus becomes a hotspot of concrete and mine filling engineering.The present work clarified some basic concepts prior to an introduction upon the characteristics of five published aggregate packing density models.Then three wall and loosening effect models which reflect the particle interactions on packing density accumulation were analyzed,and their differences and scope of application were discussed.An experimental program was developed to evaluate these models adopting waste rock aggregate in Jinchuan Nickel Mine.The result showed that,the mix ratio of coarse and fine aggregate determined the applicable model,the existing published models were incapable of accurate prediction for the real packing rate.Finally,the paper established a RBF neural network ‘black box’model which can precisely predict the real packing density,and ended with an evaluation of the new solution.更多还原