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桥梁结构非线性模型修正研究综述

王佐才 丁雅杰 戈壁 袁子青 辛宇

王佐才, 丁雅杰, 戈壁, 袁子青, 辛宇. 桥梁结构非线性模型修正研究综述[J]. 交通运输工程学报, 2022, 22(2): 59-75. doi: 10.19818/j.cnki.1671-1637.2022.02.004
引用本文: 王佐才, 丁雅杰, 戈壁, 袁子青, 辛宇. 桥梁结构非线性模型修正研究综述[J]. 交通运输工程学报, 2022, 22(2): 59-75. doi: 10.19818/j.cnki.1671-1637.2022.02.004
WANG Zuo-cai, DING Ya-jie, GE Bi, YUAN Zi-qing, XIN Yu. Review on nonlinear model updating for bridge structures[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 59-75. doi: 10.19818/j.cnki.1671-1637.2022.02.004
Citation: WANG Zuo-cai, DING Ya-jie, GE Bi, YUAN Zi-qing, XIN Yu. Review on nonlinear model updating for bridge structures[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 59-75. doi: 10.19818/j.cnki.1671-1637.2022.02.004

桥梁结构非线性模型修正研究综述

doi: 10.19818/j.cnki.1671-1637.2022.02.004
基金项目: 

国家自然科学基金项目 51922036

安徽省重点研发计划 1804a0802204

中央高校基本科研业务费专项资金项目 JZ2020HGPB0117

详细信息
    作者简介:

    王佐才(1982-),男,湖南双峰人,合肥工业大学教授,工学博士,从事桥梁结构健康监测研究

    通讯作者:

    丁雅杰(1991-),男,安徽滁州人,合肥工业大学工学博士研究生

  • 中图分类号: U441.3

Review on nonlinear model updating for bridge structures

Funds: 

National Natural Science Foundation of China 51922036

Key Research and Development Project of Anhui Province 1804a0802204

Fundamental Research Funds for the Central Universities JZ2020HGPB0117

More Information
  • 摘要: 针对桥梁服役期间由于结构力学性能减弱从而表现出具有时变特征的非线性振动问题,在回顾非线性模型修正发展的基础上,分别从非线性系统识别、非线性模型修正方法和非线性模型不确定性量化3个方面入手,总结了结构非线性模型修正技术中存在的一些关键问题;结合复杂结构损伤识别、性能评估与安全监测等内容,对其在桥梁结构中的应用展开了讨论。研究结果表明:以固有频率和模态振型为代表的响应特征量仅能反映时不变结构的物理特性,对于非线性结构而言其力学性能随外激励作用而不断变化,基于线性系统特征量的模型修正方法不能很好地适用于具有明显时变特性的非线性结构;结构动力响应主分量的瞬时频率和瞬时幅值包含了振动响应信号的相位信息和幅值信息,可以较为全面地反映动力荷载作用下结构响应的非平稳特性,选择具有时变特性的瞬时特征量来构建目标函数能够更为合理地表征非线性结构的动力特性;不确定性模型修正方法通过综合利用实测响应数据,考虑了测量噪声、模型误差和数值计算方法等不确定因素的影响,提高了模型修正结果的准确性;复杂结构非线性模型修正过程中涉及的参数众多,计算量大,极大地限制了其在实际工程结构中的应用,因此,合理选择具有代表性的非线性模型参数以及提高模型修正的计算效率是当前亟需解决的问题。

     

  • 图  1  非线性结构模型修正过程

    Figure  1.  Model updating process of nonlinear structure

    图  2  非线性结构模型修正方法技术路线

    Figure  2.  Technical route of nonlinear structural model updating method

    图  3  非线性系统识别方法

    Figure  3.  Nonlinear system identification method

    图  4  非线性系统刚度边际曲线

    Figure  4.  Stiffness marginal curves of nonlinear system

    图  5  非线性系统模式识别方法

    Figure  5.  Pattern identification method of nonlinear system

    图  6  主分量响应信号瞬时特征参数

    Figure  6.  Instantaneous characteristics parameters of principal component response signal

    图  7  SA算法流程

    Figure  7.  Flow of SA algorithm

    图  8  GA的流程

    Figure  8.  Flow of GA

    图  9  BP神经网络结构

    Figure  9.  BP neural network structure

    图  10  贝叶斯推断过程

    Figure  10.  Bayesian inference process

    图  11  考虑不确定性的非线性模型修正框架

    Figure  11.  Framework of nonlinear model updating with uncertainties

    图  12  基于区间数值计算的不确定性模型修正流程

    Figure  12.  Uncertainty model updating process based on interval numerical calculation

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  • 收稿日期:  2021-10-22
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