华南理工大学学报(自然科学版) ›› 2009, Vol. 37 ›› Issue (11): 22-26.

• 交通与运输工程 • 上一篇    下一篇

基于神经网络的道路季节分类

胡迟春1  王端宜1  Kejin Wang2  Jim Cable3   

  1. 1.华南理工大学土木与交通学院,广东广州510640; 2.爱荷华州立大学土木与环境学院,美国爱荷华州爱姆斯市50010; 3.美国国家水泥混凝土路面研究中心,美国爱荷华州爱姆斯市50010
  • 收稿日期:2008-11-03 修回日期:2009-02-04 出版日期:2009-11-25 发布日期:2009-11-25
  • 通信作者: 王端宜(1960-),男,教授,博士,主要从事路面结构与材料研究.E-mail:tcdywang@scut.edu.cn E-mail:huchichun@gmail.com
  • 作者简介:胡迟春(1982-),男,讲师,主要从事路面结构与材料方面的研究.
  • 基金资助:

    国家留学基金资助项目(2007U33002)

Seasonal Roadway Classification Based on Neural Network

Hu Chi-chun  Wang Duan-yi  Kejin Wang  Jim Cable   

  1. 1 School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, Guangdong, China; 2. College of Civil, Environmental and Construction Engineering, Iowa State University, Ames Iowa 50010, USA; 3. National Concrete Pavement Technology Center, Ames Iowa 50010, USA
  • Received:2008-11-03 Revised:2009-02-04 Online:2009-11-25 Published:2009-11-25
  • Contact: 王端宜(1960-),男,教授,博士,主要从事路面结构与材料研究.E-mail:tcdywang@scut.edu.cn E-mail:huchichun@gmail.com
  • About author:胡迟春(1982-),男,讲师,主要从事路面结构与材料方面的研究.
  • Supported by:

    国家留学基金资助项目(2007U33002)

摘要: 为了合理确定路面结构设计时的输入参数,引入自组织特征映射神经网络,结合Matlab软件对神经网络进行权值训练,将网络训练是否收敛来作为分类的依据,根据温度、交通量和降雨量等几个重要参数对道路进行季节分类,最后按照分类结果进行路面结构分析与材料设计.实践证明,该方法分类效果良好,能很好地解决路面设计参数的合理确定问题,从而大大延长路面的使用寿命,提高道路投资的经济效益.

关键词: 道路季节分类, 神经网络, 自组织特征映射, 路面结构设计

Abstract:

In order to reasonably determine the input parameters in pavement structure design, a self-organized feature mapping neural network is introduced and its weight is trained with Matlab. Then, by taking the convergence of the training as the classification rule, a seasonal roadway classification is made according to such important parameters as temperature, traffic and rainfall. Moreover, pavement structure analysis and material design are performed according to the classification results. The proposed classification method is proved effective in determining reasonable design parameters of pavement. Thus, it greatly prolongs the service life of pavement and improves the economic benefit of road investment.

Key words: seasonal roadway classification, neural network , self-organizing feature map, pavement structure design