中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (5): 164-168.doi: 10.16265/j.cnki.issn1003-3033.2017.05.029

• 公共安全 • 上一篇    下一篇

基于自适应模糊推理和RBF网络的桥梁安全评估

王彬1, 徐秀丽1 教授, 李雪红1,2 教授, 李枝军1 讲师, 张建东1 教授级高工   

  1. 1 南京工业大学 土木工程学院,江苏 南京 211816;
    2 南京工大交通科技有限公司,江苏 南京 211816
  • 收稿日期:2016-12-09 修回日期:2017-02-19 出版日期:2017-05-20 发布日期:2020-10-30
  • 作者简介:王 彬 (1991—),男,江苏南京人,硕士研究生,主要研究方向为桥梁评估和管理。
  • 基金资助:
    江苏省科技计划项目政策引导类计划(产学研合作)-前瞻性联合研究项目(BY2016005-12);江苏省交通运输科技项目(2014Y01)。

Safety assessment of bridge based on RBF neural network and adaptive fuzzy inference

WANG Bin1, XU Xiuli1, LI Xuehong1,2, LI Zhijun1, ZHANG Jiandong1   

  1. 1 College of Civil Engineering, Nanjing Tech University, Nanjing Jiangsu 211816, China;
    2 Nanjing Tech University Traffic Technology Limited Company, Nanjing Jiangsu 211816, China
  • Received:2016-12-09 Revised:2017-02-19 Online:2017-05-20 Published:2020-10-30

摘要: 为准确评估桥梁的安全性能,根据钢筋混凝土桥梁的结构特征,首先构建桥梁安全性评估指标体系, 确定评估指标的分级标准;然后使用径向基函数神经网络(RBF)替代传统的BP神经网络,优化学习速度和适用范围;其次结合自适应模糊推理,建立基于自适应RBF神经网络-模糊推理的桥梁安全性评估系统;最后用该系统评估某钢筋混凝土桥梁的安全性能。示例分析结果表明,大量专家评估意见调查数据,可为评估系统提供足够的输入数据,学习后的系统的输出结果与专家的评估意见误差减小,可用于评估桥梁的实时工作状态。

关键词: 钢筋混凝土桥梁, 安全性评估, 径向基(RBF)神经网络, 自适应模糊推理, 专家评估

Abstract: In order to accurately evaluate the safety performance of bridge, an evaluation index system was established for safety assessment of existing reinforced concrete bridges based on the structure properties. Rating standards were developed for the evaluation indexes. RBF neural network was used to replace the traditional BP neural network, improving the learning speed and scope of application. A safety evaluation system was established for existing bridges based on RBF neural network and adaptive fuzzy inference. The system was used to evaluate the safety of a certain steel concrete bridge. The results show that a large number of experts evaluation data can provide sufficient input data for the evaluation system, and that the system after studying can imitate the actual evaluation results of experts quickly and efficiently, and can accurately evaluate the actual safety condition of the bridge.

Key words: reinforced concrete bridges, safety assessment, adaptive fuzzy inference, radial basis function(RBF) neural network, expert evaluation

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