东南大学学报（英文版）2017，Vol.33Issue(4)：490-495,6.DOI:10.3969/j.issn.1003-7985.2017.04.016
基于人工神经网络的沥青路面水膜厚度预测
Prediction for asphalt pavement water film thickness based on artificial neural network
摘要
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.关键词
路面工程/水膜厚度/人工神经网络/水动力学方法/预测分析Key words
pavement engineering/water film thickness/artificial neural network/hydrodynamic method/prediction analysis分类
交通工程引用本文复制引用
马耀鲁,耿艳芬,陈先华,卢艳坤..基于人工神经网络的沥青路面水膜厚度预测[J].东南大学学报（英文版）,2017,33(4):490-495,6.基金项目
The National Natural Science Foundation of China (No.51478114,51778136),the Transportation Science and Technology Program of Liaoning Province (No.201532). （No.51478114,51778136）
