[1]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)):378-384.[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)):378-384.[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()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第34卷
期数:
2017年No.4(331-440)
页码:
378-384
栏目:
土木建筑工程
出版日期:
2017-07-10

文章信息/Info

Title:
Prediction method for asphalt pavement crack based on GRNN neural network
文章编号:
201704007
作者:
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
Author(s):
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
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
Keywords:
road engineering prediction method crack expressway asphalt concrete pavement general regression neural network
分类号:
U 416.2
DOI:
10.3724/SP.J.1249.2017.04378
文献标志码:
A
摘要:
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.
Abstract:
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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备注/Memo

备注/Memo:
Received:2017-02-11;Accepted:2017-05-23
Foundation:Science and Technology Planning Project of Transportation Department of Guangdong Province(Science-2014-02-008)
Corresponding author:Professor Chen Huaxin. E-mail: chx92070@163.com
Citation: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(4): 378-384.(in Chinese)
基金项目:广东省交通运输厅科技计划资助项目(科技- 2014-02-008)
作者简介:柯文豪(1986—),男,长安大学博士研究生. 研究方向:基于路用性能的路面设计方法. E-mail: kwh860225@126.com
引文: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[J]. 深圳大学学报理工版,2017,34(4):378-384.
更新日期/Last Update: 2017-06-26