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铁道科学与工程学报

JOURNAL OF RAILWAY SCIENCE AND ENGINEERING

第11卷    第3期    总第59期    2014年6月

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文章编号:1672-7029(2014)03-0107-04
客运专线铁路路基粗粒土填料最大干密度的BP神经网络预测
刘源1,宋晓东2,聂志红1,王翔1

(1.中南大学 土木工程学院,湖南 长沙 410075;
2.沪昆客专湖南有限公司,湖南 长沙 410018
)

摘 要: 以沪昆客运专线芷江北站粗粒土填料为研究对象,通过表面振动压实试验,得到不同颗粒级配下试样的最大干密度。考虑各粒径含量与最大干密度的非线性关系,将粒度成分,级配指标,分形指标作为网络输入层,基于误差反向传播算法,以干密度试验结果为训练样本,建立BP神经网络预测模型,实现对不同颗粒级配下粗粒土最大干密度的预测。

 

关键字: 粗粒土;最大干密度;神经网络;预测

Prediction model of maximum dry density of coarse grained soil using BP neural networks
LIU Yuan1,SONG Xiaodong2,NIE Zhihong1,WANG Xiang1

1. School of Civil Engineering,Central South University,Changsha 410075,China;
2. Shanghai-Kunming Passenger Railway Hunan Co., Ltd., Changsha 410018,China

Abstract:Taking the fillers of the coarse-grained soil in Zhijiang north station of Shanghai-Kunming passenger dedicated line as the research object, the vibration compaction test was conducted to study maximum dry densities under different granular compositions. Considering the non-linear relations between granular compositions and maximum dry densities, a BP neural network prediction model of which the input layer was consisted of granular compositions, grading index and fractal index was established. Based on the backwards error propagation algorithm and the result of the maximum dry density test, the model established in this paper performs well in predicting the maximum dry density of the coarse-grained soil of various granular compositions.

 

Key words: coarse-grained soil; maximum dry density; neural network; prediction

ISSN 1672-7029
CN 43-1423/U

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