| 小波分析和RBF神经网络在地基沉降预测中的应用研究 |
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| 引用本文： | 李长冬,唐辉明,胡斌,李东明,倪俊.小波分析和RBF神经网络在地基沉降预测中的应用研究[J].岩土力学,2008,29(7):1917-1922. |
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| 作者姓名： | 李长冬 唐辉明 胡斌 李东明 倪俊 |
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| 作者单位： | 1.中国地质大学 工程学院，武汉 430074；2.中国地质大学 机械与电子工程学院，武汉 430074；3.镇江市国土资源局，江苏 镇江 212000 |
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| 基金项目： | 国家自然科学基金
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中国博士后科学基金
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湖北省博士后科研项目
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中国地质大学校科研和教改项目
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中国地质丈学(武汉)优秀青年教师资助计划
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湖北省武汉市青年科技晨光计划 |
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| 摘 要： | 地基沉降是一种危害很大的环境灾害。地基沉降的监测数据经常受降雨及工程施工等诸多外界因素的干扰，故而在沉降曲线中存在许多数据突变点。为此，提出基于小波分析与RBF神经网络相结合的新的地基沉降预测方法，首先采用小波分析对对原始监测数据进行数据去噪处理，进而得到反映实际变化的地基沉降曲线，然后采用径向基函数（RBF）神经网络方法对其进行预测，为工程设计提供依据。最后结合工程实例分析，通过多种小波去噪与预测结果的对比研究，表明3次B样条小波的去噪及预测效果最好，与实测值能较好地吻合，具有较好的工程应用前景。
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| 关 键 词： | 地基沉降 小波分析 RBF神经网络 3次B样条小波 预测 |
| 收稿时间： | 2007-01-05 |
Research on application of wavelet analysis and RBF neural network to prediction of foundation settlement |
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| LI Chang-dong,TANG Hui-ming,HU Bin,LI Dong-ming,Ni Jun.Research on application of wavelet analysis and RBF neural network to prediction of foundation settlement[J].Rock and Soil Mechanics,2008,29(7):1917-1922. |
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| Authors: | LI Chang-dong TANG Hui-ming HU Bin LI Dong-ming Ni Jun |
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| Institution: | 1. Faculty of Engineering, China University of Geosciences, Wuhan 430074, China; 2. Faculty of Mechanical and Electronic Engineering, China University of Geosciences, Wuhan 430074, China; 3. The Land and Resources Department of Zhenjiang, Zhenjiang 212000, China |
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| Abstract: | Foundation settlement is a kind of severe environmental hazard. The monitoring data of foundation settlement are usually disturbed by rainfall and engineering construction. As a result, there are a lot of data breakpoints in the settlement curve. Therefore, based on wavelet analysis and RBF neural network theory, a new method for foundation settlement is proposed. Firstly, based on the de-noising process of monitoring data by wavelet analysis, the foundation settlement curve that is close to the practical situation can be obtained. Afterwards, the prediction is carried on by radial basic function (RBF) neural network method. The wavelet analysis and RBF neural network method can provide engineering design with scientific basis. Finally, based on the engineering instance analysis and the contrast study between different kinds of wavelets, the results show that the de-noising and prediction effect of triple B-spline wavelet is the best among the chosen wavelets, and it has a good future in the field of engineering application. |
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| Keywords: | foundation settlement wavelet analysis RBF neural network triple B-spline wavelet prediction |
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