Automatic identification of modal parameters of long-span bridges considering uncertainty

被引：0
|
作者：
Su X. [1 ]
Mao J. [1 ]
Wang H. [1 ]
Yang C. [1 ]
机构：
[1] Key Laboratory of Concrete and Prestressed Concrete Structures, Ministry of Education, Southeast University, Nanjing
来源：
Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2023年 / 53卷 / 05期
关键词：
:long-span bridge; Bayesian inference; deep learning; modal parameters; online analysis;
D O I：
10.3969/j.issn.1001-0505.2023.05.012
中图分类号：
学科分类号：
摘要：
To improve the reliability of online tracking of modal parameters of long-span bridges,an automatic identification method of modal parameters considering uncertainty was developed by integrating deep learning and Bayesian inference. Firstly,the detection and repair of abnormal monitoring data were carried out using the three times standard deviation method and generalized regression neural networks. Then,a convolutional autoencoder (CAE)was used to remove the noise information embedded in the cross-modal assurance criterion (CMAC)matrix and extract the target modal response intervals. The cluster analysis was performed by Kohonen network to identify the modal number in the frequency response intervals. Finally,the operational modes were identified based on Bayesian inference. The reliability assessment of the identification results was carried out according to the coefficient of variation. The method was utilized to identify and statistically analyze the modal parameters of the Sutong Bridge. The results show that the proposed method can automatically identify and accurately track bridge modal parameters. It can be applied to bridge monitoring data for anomaly data processing,modal parameter identification and reliability assessment of the results. The lognormal distributions fit well with the probability distributions of the frequency and the damping ratio. © 2023 Southeast University. All rights reserved.
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页码：850 / 856
页数：6
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