|Table of Contents|

[1] Ma Yaolu, Geng Yanfen, Chen Xianhua, Lu Yankun, et al. Prediction for asphalt pavement water film thicknessbased on artificial neural network [J]. Journal of Southeast University (English Edition), 2017, 33 (4): 490-495. [doi:10.3969/j.issn.1003-7985.2017.04.016]
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Prediction for asphalt pavement water film thicknessbased on artificial neural network()
基于人工神经网络的沥青路面水膜厚度预测
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
33
Issue:
2017 4
Page:
490-495
Research Field:
Traffic and Transportation Engineering
Publishing date:
2017-12-30

Info

Title:
Prediction for asphalt pavement water film thicknessbased on artificial neural network
基于人工神经网络的沥青路面水膜厚度预测
Author(s):
Ma Yaolu1, Geng Yanfen1, Chen Xianhua1, Lu Yankun2
1School of Transportation, Southeast University, Nanjing 210096, China
2Liaoning Provincial Transportation Planning and Design Institute Co., Ltd., Shenyang 110000, China
马耀鲁1, 耿艳芬1, 陈先华1, 卢艳坤2
1东南大学交通学院, 南京 210096; 2辽宁省交通规划设计院有限责任公司, 沈阳 110000
Keywords:
pavement engineering;  water film thickness;  artificial neural network;  hydrodynamic method;  prediction analysis
路面工程;  水膜厚度;  人工神经网络;  水动力学方法;  预测分析
PACS:
U416.2
DOI:
10.3969/j.issn.1003-7985.2017.04.016
Abstract:
In order to study the variation of the asphalt pavement water film thickness influenced by multi-factors, a new method for predicting water film thickness was developed by the combination of the artificial neural network(ANN)and two-dimensional shallow water equations based on hydrodynamic theory. Multi-factors included the rainfall intensity, pavement width, cross slope, longitudinal slope and pavement roughness coefficient. The two-dimensional hydrodynamic method was validated by a natural rainfall event. Based on the design scheme of Shen-Shan expressway engineering project, the limited training data obtained by the two-dimensional hydrodynamic simulation model was used to predict water film thickness. Furthermore, the distribution of the water film thickness influenced by multi-factors on the pavement was analyzed. The accuracy of the ANN model was verified by the 18 sets of data with a precision of 0.991. The simulation results indicate that the water film thickness increases from the median strip to the edge of the pavement. The water film thickness variation is obviously influenced by rainfall intensity. Under the condition that the pavement width is 20 m and the rainfall intensity is 30 mm/h, the water film thickness is below 10 mm in the fast lane and 20 mm in the lateral lane. Although there is fluctuation due to the amount of training data, compared with the calculation on the basis of the existing criterion and theory, the ANN model exhibits a better performance for depicting the macroscopic distribution of the asphalt pavement water film.
为了研究多因素影响下沥青路面水膜厚度的变化, 结合基于水动力学理论的二维浅水方程, 提出一种利用人工神经网络(ANN)预测沥青路面水膜厚度的方法.多因素包括降雨强度、路面宽度、路面横坡、路面纵坡和路面粗糙系数.二维水动力仿真模型经过实测数据验证并根据沈山高速公路工程设计方案仿真得到有限数量的训练数据用于沥青路面水膜厚度的预测, 进而分析了多因素对水膜厚度在路面分布的影响.经过18组数据的验证, 人工神经网络模型预测精度可达0.991.预测结果表明:水膜厚度从中央分隔带向道路边缘逐渐增加, 降雨强度对水膜厚度的变化有明显影响.在路面宽度20 m, 降雨强度30 mm/h的条件下, 路面内侧车道内的水膜厚度低于10 mm, 外侧车道的水膜厚度为20 mm.受训练样本数量的影响, 预测结果存在一定的波动, 但与现行规范和理论计算值相比, 人工神经网络模型能够更好地描述沥青路面水膜的宏观分布特性.

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Memo

Memo:
Biographies: Ma Yaolu(1989—), male, graduate; Geng Yanfen(corresponding author), female, doctor, associate professor, yfgeng@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.51478114, 51778136), the Transportation Science and Technology Program of Liaoning Province(No.201532).
Citation: Ma Yaolu, Geng Yanfen, Chen Xianhua, et al. Prediction for asphalt pavement water film thickness based on artificial neural network[J].Journal of Southeast University(English Edition), 2017, 33(4):490-495.DOI:10.3969/j.issn.1003-7985.2017.04.016.
Last Update: 2017-12-20