| 基于L-M法BP神经网络的高填路堤地基沉降预测 |
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| 引用本文： | 於永和,李素艳.基于L-M法BP神经网络的高填路堤地基沉降预测[J].交通标准化,2006(10):167-171. |
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| 作者姓名： | 於永和 李素艳 |
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| 作者单位： | 1. 广西工学院土木建筑系,广西,柳州,545006
2. 同济大学交通运输工程学院,上海,200092 |
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| 摘 要： | 针对高填路堤地基沉降预测中影响因素众多且存在高度的非线性，难以用解析式表达等特点，提出采用基于L—M（Levenberg—Marquardt）的BP神经网络法对高填方地基沉降进行预测，并通过对工程实例的网络训练和网络检验，得出BP神经网络计算值与实测值十分接近的结论，可充分证明L—M法BP神经网络在高路堤地基沉降预测中具有很好的实用价值。
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| 关 键 词： | 高填路堤 地基沉降预测 L-M法 BP神经网络 |
| 文章编号： | 1002-4786（2006）10-0167-04 |
| 收稿时间： | 2006-04-28 |
| 修稿时间： | 2006年4月28日 |
Foundation Settlement Prediction Based on BP Neural Networks by L-M Method for High-filled Embankment |
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| YU Yong-he,LI Su-yan.Foundation Settlement Prediction Based on BP Neural Networks by L-M Method for High-filled Embankment[J].Communications Standardization,2006(10):167-171. |
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| Authors: | YU Yong-he LI Su-yan |
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| Institution: | 1Department of Civil Engineering, Guangxi University of Technology, Liuzhou 545006, China; 2.Shool of Traffic and Transportation, Tongji University, Shanghai 200092, China |
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| Abstract: | In the prediction of foundation settlement for high-filled embankment, there are many affecting factors and most of them are high nonlinear, therefore, it is difficult to express with analytical formulas. Aiming at these characteristics, BP neural network based on L-M method can be put forward to predict foundation settlement. It trains and tests the network with engineering examples, and the results show that network calculation value is close to actual survey value, which indicates that L-M based on BP neural network is practical in prediction of foundation settlement for high-filled embankment. |
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| Keywords: | high-filled embankment foundation settlement prediction Levenberg-Marquardt method BP neural network |
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