华南理工大学学报(自然科学版) ›› 2004, Vol. 32 ›› Issue (9): 72-75,96.

• • 上一篇    下一篇

用人工神经网络方法评估桥梁缺损状况

韩大建 杨炳尧 颜全胜   

  1. 华南理工大学 建筑学院‚广东 广州510640
  • 收稿日期:2004-02-25 出版日期:2004-09-20 发布日期:2015-09-09
  • 通信作者: 韩大建(1940-)‚女‚教授‚主要从事建筑与桥梁结构方面的研究。 E-mail:ardjhan@scut.edu.cn
  • 作者简介:韩大建(1940-)‚女‚教授‚主要从事建筑与桥梁结构方面的研究。
  • 基金资助:
    广州市科技攻关引导项目(2002Z3-D3031)

An Artificial Neural Network Method to Evaluate Bridge Damage Conditions

Han Da- jian  Yang Bing- yao  Yan Quan- sheng   

  1. College of Architecture and Civil Engineering‚South China Univ.of Tech.‚Guangzhou510640‚Guangdong‚China
  • Received:2004-02-25 Online:2004-09-20 Published:2015-09-09
  • Contact: 韩大建(1940-)‚女‚教授‚主要从事建筑与桥梁结构方面的研究。 E-mail:ardjhan@scut.edu.cn
  • About author:韩大建(1940-)‚女‚教授‚主要从事建筑与桥梁结构方面的研究。

摘要: 针对现有桥梁评估方法存在的不足‚介绍了一种应用神经网络进行桥梁缺损状况评估的方法‚并对几种常见的人工神经网络模型的评估效果进行了比较.利用广东省内1018座桥梁的养护数据‚对神经网络进行训练和测试‚发现使用神经网络对桥梁进行评估‚能够取得比较好的评估效果.使用神经网络方法对桥梁“等级”进行评估‚其准确率超过60%‚平均每座桥的评估误差为0.25个等级.

关键词: 人工神经网络, 桥梁评估, 学习向量量化网络, 径向基网络, Elman 网络

Abstract: In view of the weakness of existing bridge evaluation methods‚a neural network method was first intro-duced to evaluate the damage conditions of a bridge.The evaluation effects of several common artificial neural network (ANN) models were then compared.The ANN models were finally trained and tested based on the maintenance data of1018bridges on the nationa- l grade roads in Guangdong.It is found that the neural network method is effective in e-valuating the bridge conditions‚more than60% of the bridge grade being correctly evaluated and the average evaluation error of each bridge being0.25grades.

Key words: artificial neural network, bridge evaluation, LVQ network, RBF network, Elman network

中图分类号: