|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]

Prediction for asphalt pavement water film thicknessbased on artificial neural network()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2017 4
Research Field:
Traffic and Transportation Engineering
Publishing date:


Prediction for asphalt pavement water film thicknessbased on artificial neural network
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
pavement engineering water film thickness artificial neural network hydrodynamic method prediction analysis
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.


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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