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

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

王佐才^{1, 2,},
丁雅杰^1, ,,
戈壁¹,
袁子青¹,
辛宇^{1, 3}

1.
合肥工业大学土木与水利工程学院，安徽合肥 230009
2.
合肥工业大学土木工程防灾减灾安徽省工程技术研究中心，安徽合肥 230009
3.
合肥工业大学安徽省基础设施安全检测与监测工程实验室，安徽合肥 230009

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
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出版历程
- 收稿日期: 2021-10-22
- 刊出日期: 2022-04-25

Review on nonlinear model updating for bridge structures

WANG Zuo-cai^{1, 2
,},
DING Ya-jie^{1
, ,},
GE Bi¹,
YUAN Zi-qing¹,
XIN Yu^{1, 3}

1.
College of Civil Engineering, Hefei University of Technology, Hefei 230009, Anhui, China
2.
Engineering Technology Research Center of Disaster Prevention and Mitigation of Civil Engineering of Anhui Province, Hefei University of Technology, Hefei 230009, Anhui, China
3.
Anhui Province Infrastructure Safety Inspection and Monitoring Engineering Laboratory, Hefei University of Technology, Hefei 230009, Anhui, China

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

Author Bio:
WANG Zuo-cai(1982-), male, professor, PhD, wangzuocai@hfut.edu.cn

DING Ya-jie(1991-), male, doctoral student, dingyajie@mail.hfut.edu.cn

摘要

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。
- 桥梁工程 /
- 非线性模型修正 /
- 非线性模式识别 /
- 不确定性量化 /
- 贝叶斯理论 /
- 优化算法
Abstract: Due to the weakening of the structural mechanical properties during the service period of bridges, the nonlinear vibration with time-varying characteristics occurs. Considering this, the development of nonlinear model updating was reviewed, and on this basis, some critical problems existing in nonlinear model updating technologies were summarized from the aspects of nonlinear system identification, nonlinear model updating methods, and uncertainty quantification of nonlinear models. In addition, in view of the damage identification, performance assessment, and safety monitoring of complex structures, the application of nonlinear model updating in bridge structures was further discussed. Research results indicate that the response characteristic quantities represented by natural frequencies and modes of vibration can only reflect the physical characteristics of time-invariant structures. However, for nonlinear structures, their mechanical properties change with the external excitation, and thus the model updating methods based on characteristic quantities of linear systems are not suitable for nonlinear structures with obvious time-varying characteristics. The instantaneous frequency and amplitude of the principal component of structural dynamic response contain the phase and amplitude information of vibration response signals. They can comprehensively reflect the non-stationary characteristics of structural responses under dynamic loads. The dynamic characteristics of nonlinear structures can be properly represented by using the instantaneous characteristic quantities with time-varying characteristics to construct the objective function. With the consideration of uncertainty factors, such as the measurement noise, model errors, and numerical calculation methods, the uncertainty model updating method can improve the model updating result by comprehensively using the measured response data. Since many parameters and massive computations are involved in the nonlinear model updating of complex structures, its application in the practical engineering structures is greatly limited. Therefore, the reasonable selection of representative nonlinear model parameters and improving the computational efficiency are urgent problems to be solved. 12 figs, 95 refs.
- bridge engineering /
- nonlinear model updating /
- nonlinear pattern recognition /
- uncertainty quantification /
- Bayesian theorem /
- optimization algorithm

HTML全文

图 1 非线性结构模型修正过程

Figure 1. Model updating process of nonlinear structure

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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

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图 3 非线性系统识别方法

Figure 3. Nonlinear system identification method

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图 4 非线性系统刚度边际曲线

Figure 4. Stiffness marginal curves of nonlinear system

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图 5 非线性系统模式识别方法

Figure 5. Pattern identification method of nonlinear system

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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

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图 7 SA算法流程

Figure 7. Flow of SA algorithm

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图 8 GA的流程

Figure 8. Flow of GA

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图 9 BP神经网络结构

Figure 9. BP neural network structure

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图 10 贝叶斯推断过程

Figure 10. Bayesian inference process

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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

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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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