期刊论文

中文

湘潭大学学报(自然科学学报)

2096-644X

2012

34

1

54-58

10.3969/j.issn.1000-5900.2012.01.012

国家自然科学基金项目（10672191）； 湖南省自然科学基金重点项目（06JJ2059）；

本校为第一机构

针对高填路堤地基沉降难以预测这一技术难题,对其影响地基沉降的主要因素进行了分析,根据各因素之间存在的高度非线性,结合BC-RBFNN（基于聚类分析径向基函数神经网络）非线性拟合的特点,提出一种基于BC-RBNN模型对高填方地基沉降进行预测.运用施工期路基沉降实测资料,对神经网络模型进行学习、训练和仿真,得出仿真值与实测值非常相似,从而得出基于BC-RBFNN模型在高填路堤地基沉降预测中具有很好的实用效率.

Aimed at the technical difficulty that foundation settlement of high-filled embankment is hard to predict,the author analyzed the main factors influencing foundation settlement,and proposed a BC-RBFNN（Based on Clustering Radial Basis Function Neural Network） model to predict the foundation settlement for high-filled embankment according to the non-linear of each main influencing factor.Then,the author used the model to learn,train and emulate based on the monitoring data of settlement during construction.The results indicate the emulating dat...