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Prediction method for asphalt pavement crack based on GRNN neural network(PDF)


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Prediction method for asphalt pavement crack based on GRNN neural network
Ke Wenhao1 Chen Huaxin1 Lei Yu2 and Zhang Tao2
1) School of Materials Science and Engineering, Chang’an University, Xi’an 710064, Shaanxi Province, P.R.China
2) China Communications Construction Company First Highway Consultants Company Limited, Xi’an 710065, Shaanxi Province, P.R.China
road engineering prediction method crack expressway asphalt concrete pavement general regression neural network
U 416.2
The relationship between crack and influencing factors is analyzed by using correlation analysis method. The prediction model for crack of asphalt pavement is established by using of general regression neural network (GRNN). In order to establish the model, 50 sets of measured data of expressway pavement are selected for determining model parameters, and 6 sets of measured data are selected for model validation. The service life and the cumulative number of standard axle loads are highly positive correlated with crack. The asphalt concrete layer thickness, the semi-rigid structural layer thickness and the surface layer asphalt content are moderately negative correlated with crack. The bottom layer asphalt content is low positively correlated with crack. The correlation between annual minimum temperature and crack is weak. The deviation of predicted values and measured ones is small. The maximum deviation is 12.71%, which shows that the model is feasible.


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Last Update: 2017-06-26