A Soft-roadbed Settlement Prediction Model Based on RBFNN

被引：0
|
作者：
Peng, Xianghua [2 ]
Wang, Zhichao [3 ]
Yu, Min [1 ]
机构：
[1] Cent South Univ Forestry & Technol, Inst Rheol Mech & Mat Engn, Changsha 410004, Hunan, Peoples R China
[2] Cent South Univ Forestry & Technol, Swan Coll, Changsha 410004, Hunan, Peoples R China
[3] Xiangtan Univ, Coll civil Engn & Mech, Xiangtan, Hunan 411105, Peoples R China
来源：
ADVANCES IN CIVIL INFRASTRUCTURE ENGINEERING, PTS 1 AND 2 | 2013年 / 639-640卷
关键词：
Soft-roadbed; subgrade settlement; prediction model; RBF Neural Network;
D O I：
10.4028/www.scientific.net/AMR.639-640.535
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
The control of subgrade settlement has been the main influencing factor on highroad pavement quality, so estimating settlement amount after construction based on designed embankment height and planning the construction filling height beforehand through the settlement prediction is a very important work. Because of the complex coupling properties and high nonlinear characteristics of the factors influencing foundation settlement, a soft-roadbed settlement prediction model based on RBFNN (Radial Basis Function Neural Network) is proposed, combining the global fitting properties of RBFNN. The analysis shows that the model owns good fitting property and high prediction precise, and certain practical value.
引用
收藏
页码：535 / +
页数：2
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