| 基于人工神经网络的道路表面降雨水深预测 |
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| 引用本文： | 季天剑,安景峰,何申明,李春雷. 基于人工神经网络的道路表面降雨水深预测[J]. 南京航空航天大学学报(英文版), 2006, 23(2): 115-119 |
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| 作者姓名： | 季天剑 安景峰 何申明 李春雷 |
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| 作者单位： | 南京航空航天大学航空宇航学院,南京,210016,中国;江苏省高速公路建设指挥部,南京,210004,中国;盐城市交通规划设计院,盐城,224001,中国;江苏沿江高速公路有限公司,常熟,215500,中国 |
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| 基金项目： | 国家西部交通建设科技项目 |
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| 摘 要： | 建立了基于人工神经网络的道路表面水膜厚度预测模型，通过试验数据的训练确定权重和阈值，经过检验样本的检验，能够很好地预测道路表面的水膜厚度。结果表明，本文建立的人工神经网络模型用于道路表面水膜厚度预估可行。
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| 关 键 词： | 坡面流 混合料级配 人工神经网络 道路表面水深 |
| 收稿时间： | 2005-05-20 |
| 修稿时间： | 2005-10-08 |
DEPTH PREDICTION OF RAIN WATER ON ROAD SURFACE BASED ON ARTIFICIAL NEURAL NETWORK |
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| Ji Tianjian,An Jingfeng,He Shenming,Li Chunlei. DEPTH PREDICTION OF RAIN WATER ON ROAD SURFACE BASED ON ARTIFICIAL NEURAL NETWORK[J]. Transactions of Nanjing University of Aeronautics & Astronautics, 2006, 23(2): 115-119 |
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| Authors: | Ji Tianjian An Jingfeng He Shenming Li Chunlei |
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| Abstract: | A model based on the non-linear artificial neural network (ANN) is established to predict the thickness of the water film on road surfaces. The weight and the threshold can be determined by training test data, and the water film thickness on the road surface can be accurately predicted by the empirical verification based on sample data. Results show that the proposed ANN model is feasible to predict the water film thickness of the road surface. |
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| Keywords: | overland flow gradation of asphalt mixture artificial neural network depth of rain water 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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