混合骨料堆积模型研究与新型模型建立

doi:10.11896/j.issn.1005-023X.2018.14.014

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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.

Key words： packing density model packing rate packing density combined aggregate RBF neural network

Published: 31 July 2018

CLC number:	TU528
	TD853

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	Articles by authors
	XIAO Bolin
	YANG Zhiqiang
	GAO Qian
	GUO Xiaodong

Cite this article:

XIAO Bolin,YANG Zhiqiang,GAO Qian, et al. Discussion on Packing Density Models of Combined Aggregate and a New Solution[J]. Materials Reports, 2018, 32(14): 2400-2406.

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http://www.mater-rep.com/EN/10.11896/j.issn.1005-023X.2018.14.014 OR http://www.mater-rep.com/EN/Y2018/V32/I14/2400

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