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.
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.
作者
柯文豪
陈华鑫
雷宇
张涛
Ke Wenhao Chen Huaxin Lei Yu Zhang Tao(School of Materials Science and Engineering, Chang＇an University, Xi＇an 710064, Shaanxi Province, P. R. China ） China Communications Construction Company First Highway Consultants Company Limited, Xi＇an 710065, Shaanxi Province, P. R. China)
出处
《深圳大学学报（理工版）》
EI
CAS
CSCD
北大核心
2017年第4期378-384,共7页
Journal of Shenzhen University(Science and Engineering)
基金
广东省交通运输厅科技计划资助项目(科技-2014-02-008)~~
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
road engineering
prediction method
crack
expressway
asphalt concrete pavement
general regression neural network
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
Corresponding author： Professor Chen Huaxin. E-mail ： chx92070@ 163.com
