深圳大学学报理工版

柯文豪,陈华鑫,雷宇,等.基于GRNN神经网络的沥青路面裂缝预测方法[J].深圳大学学报理工版,2017,34(No.4(331-440)):378-377.[doi:10.3724/SP.J.1249.2017.04378]
Ke Wenhao,Chen Huaxin,Lei Yu,et al.Prediction method for asphalt pavement crack based on GRNN neural network[J].Journal of Shenzhen University Science and Engineering,2017,34(No.4(331-440)):378-377.[doi:10.3724/SP.J.1249.2017.04378]

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基于GRNN神经网络的沥青路面裂缝预测方法

柯文豪¹,陈华鑫¹,雷宇²,张涛²

1)长安大学材料科学与工程学院,陕西西安 710064; 2)中交第一公路勘察设计研究院有限公司,陕西西安 710065

关键词：道路工程; 预测方法; 裂缝; 高速公路; 沥青路面; 广义回归神经网络

Prediction method for asphalt pavement crack based on GRNN neural network

Ke Wenhao¹, Chen Huaxin¹, Lei Yu², and Zhang Tao²

Ke Wenhao¹, Chen Huaxin¹, Lei Yu², and Zhang Tao²1)School of Materials Science and Engineering, Chang'an University, Xi'an 710064, Shaanxi Province, P.R.China2)China Communications Construction Company First Highway Consultants Company Limited, Xi'an 710065, Shaanxi Province, P.R.China

Keywords： road engineering; prediction method; crack; expressway; asphalt concrete pavement; general regression neural network

DOI: 10.3724/SP.J.1249.2017.04378

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参考文献

采用相关分析法对沥青路面裂缝的不同影响因素进行分析,采用广义回归神经网络(general regression neural network,GRNN))建立沥青路面裂缝预测模型,选用50组高速公路路面实测数据对模型进行训练,选用6组实测数据对模型进行检验. 结果表明,使用年限和累计轴载次数与裂缝高度正相关; 沥青层厚度、半刚性结构层厚度和上面层沥青用量与裂缝呈中度负相关; 下面层沥青用量与裂缝呈低度正相关; 年最低气温与裂缝相关性极弱.预测值与实测值偏差较小,裂缝预测值与实测值最大偏差为12.71%,说明模型预测效果较好.

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.

引言
1 沥青路面裂缝影响因素分析
2 裂缝预测
3 结论

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