| 降雨影响下高边坡渗压神经网络监测模型 |
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| 引用本文： | 黄铭,刘俊.降雨影响下高边坡渗压神经网络监测模型[J].上海交通大学学报,2013,47(10):1548-1551. |
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| 作者姓名： | 黄铭 刘俊 |
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| 作者单位： | (1.合肥工业大学 土木与水利工程学院， 合肥 230009； 2.上海交通大学 船舶海洋与建筑工程学院， 上海 200240) |
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| 摘 要： | 为准确揭示高边坡在降雨影响下的渗压变化规律，掌握其安全状态，在降雨作用分析基础上，提出以积分型降雨因子进行边坡渗压分析；以径向基函数(RBF)神经网络为建模工具，构建渗压降雨监测模型结构，并根据高密度采集的实测序列与模糊C均值聚类（FCM）算法进行RBF计算中心的比较选择.应用表明，积分型降雨因子能有效反映降雨的作用，以实测数据建立的渗压监测模型取得了理想效果.
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| 关 键 词： | 高边坡 渗压 降雨 径向基函数 监测模型 |
| 收稿时间： | 2012-11-05 |
Seepage Pressure Neural Network Monitoring Model forHigh Slope Considering the effect of Rainfall |
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| HUANG Ming,LIU Jun.Seepage Pressure Neural Network Monitoring Model forHigh Slope Considering the effect of Rainfall[J].Journal of Shanghai Jiaotong University,2013,47(10):1548-1551. |
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| Authors: | HUANG Ming LIU Jun |
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| Institution: | (1.School of Civil Engineering, Hefei University of Technology, Hefei 230009, China; 2.School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China)
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| Abstract: | In order to describe the seepage pressure regular pattern of high slope affected by rainfall, and get to know its safety state, integral rainfall factor was presented into these analysis. The monitoring model frame based on Radial basis function (RBF) artificial neural network was constructed considering the integral rainfall factor. RBF centers were confirmed by the fuzzy c means algorithm (FCM) with the observed data. Application shows that the integral rainfall factor can effectively reflect the rainfall effect, and the monitoring model achieve good training and forecasting results. |
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| Keywords: | high slope seepage pressure rainfall radial basis function (RBF) monitoring model |
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