| 道路表面水膜厚度预测模型 |
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| 引用本文： | 季天剑,黄晓明,刘清泉,唐国奇.道路表面水膜厚度预测模型[J].交通运输工程学报,2004,4(3):1-3. |
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| 作者姓名： | 季天剑 黄晓明 刘清泉 唐国奇 |
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| 作者单位： | 1. 东南大学,交通学院,江苏,南京,210096
2. 交通部公路科学研究所,北京,100088 |
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| 基金项目： | 国家西部交通建设科技项目(200131822332) |
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| 摘 要： | 针对道路表面的坡面水流受降雨和坡面粗糙程度的影响，是一个高度非线性空间分布的过程，一般模型很难精确描述。建立了基于人工神经网络的道路表面水膜厚度预测模型，以降雨强度、坡度、坡长和坡面的粗糙程度为输入层，水膜厚度为输出层，隐含层为6个神经元，通过试验数据的训练，确定了网络的权重和阈值。应用结果表明该模型预测的水膜厚度与测量值的相关系数为0．98，误差平方和为3．08，这说明该模型用于道路表面水膜厚度预估是可行的。
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| 关 键 词： | 道路工程 坡面水流 人工神经网络 路面水膜厚度 |
| 文章编号： | 1671-1637(2004)03-0001-03 |
| 修稿时间： | 2003年10月17 |
Prediction model of rain water depth on road surface |
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| JI Tian-jian,HUANG Xiao-ming,LIU Qing-quan,TANG Guo-qi.Prediction model of rain water depth on road surface[J].Journal of Traffic and Transportation Engineering,2004,4(3):1-3. |
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| Authors: | JI Tian-jian HUANG Xiao-ming LIU Qing-quan TANG Guo-qi |
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| Institution: | JI Tian-jian~1,HUANG Xiao-ming~1,LIU Qing-quan~2,TANG Guo-qi~2 |
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| Abstract: | Rainfall and slope rough degree have great effect on rain water depth on road surface, and their relation is highly nonlinear, it can not be described by simple models. This paper used artificial neural network to establish the prediction model of water film thickness. Input layers of the model were rainfall intensity, gradient, slope length and contexture depth of road, output layer was rain water depth, there were 6 neural cells in hidden layer. The weights and thresholds of the model were attained through training neural network with testing data.Applied results show that the relative coefficient of predictive values and measure values is 0.98, error is 3.08. The result indicates that the predictive model is reasonable. 2 tabs, 1 fig, 8 refs. |
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| Keywords: | highway engineering overland flow artificial neural network rain water depth on road surface |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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