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.
In order t get to know its saf model frame based the integral rainfall o describe the seepage pressure regular pattern of high slope affected by rainfall, and ety state, integral rainfall factor was presented into these analysis. The monitoring on Radial basis function （RBF） artificial neural network was constructed considering factor. RBF centers were confirmed by the fuzzy c-means algorithm （FCM） with the observed data. Application shows that the integral rain{all factor can effectively reflect the rainfall effect, and the monitoring model achieve good training and forecasting results.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2013年第10期1548-1551,共4页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金资助项目(50979056)
关键词
高边坡
渗压
降雨
径向基函数
监测模型
high slope
seepage pressure
rainfall
radial basis function （RBF）
monitoring model
