Identification of vibration displacement and dynamic characteristics of piers based on the kernelized correlation filters algorithm

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Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (8) : 267-275.

CHEN Liangyu1, CAI Wei1, XIE Wen*1, HE Tiantao2,3

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Abstract

Evaluating the mechanical performance and operational conditions of bridge structures relies on accurately measuringement of vibration displacement. Such measurement can provide essential parameters, such as mode and frequency, to assess the operational status and condition of damaged bridge structures. However, traditional displacement monitoring techniques have high cost, low accuracyare expensive, have low accuracy, and offer and limited measurement positions. This paper proposes a A low-cost, non-contact, and multi-point measurement method was proposed in this paper,based on the Kernelized correlation filters (KCF) algorithm to measure the vibration displacement of bridges. The proposed method used shaking Shaking table tests on a dual-column pier model were conducted ,with energy dissipation links under different white noise sweeps. The recorded vibration displacement from a laser displacement sensor (LDS) was used as a reference for comparison. The natural frequencies and mode shapes of the dual-column pier and bridges were identified using the covariance-driven stochastic subspace identification method. The study verified the The reliability, feasibility, and accuracy of machine vision technology in identifying the natural frequencies and mode shapes of bridges were verified. The results showed that the small amplitude vibration displacement of the dual-column pier identified by the KCF algorithm is almost consistent with the waveforms, change trends, and peak values recorded by LDS, with a maximum peak error of 4%. The error between the natural frequencies identified by the KCF algorithm and the LDS results was within 2.5%. The confidence level of the mode shapes identified between both approaches was above 0.90.

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Machine vision / Modified kernelized correlation filters（KCF) algorithm / Small amplitude vibration displacement / Dynamic properties / Shaking table tests

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CHEN Liangyu1, CAI Wei1, XIE Wen1, HE Tiantao2, 3. Identification of vibration displacement and dynamic characteristics of piers based on the kernelized correlation filters algorithm[J]. Journal of Vibration and Shock, 2025, 44(8): 267-275

References

[1] 于洋.新时期公路桥梁检测质量控制与检测技术应用研究[J].工程建设与设计,2024(02):191-193.DOI:10.13616/j.cnki.gcjsysj.2024.01.260.
YU Y. Research on quality control and application of highway bridge inspection technology in the new period [J]. Construction & Design for Engineering, 2024 (02): 191-193. DOI: 10.13616/j.cnki.gcjsysj.2024.01.260.
[2] 殷杰, 张宇峰, 彭家意. 基于监测数据标准场构建的桥梁结构状态评估方法研究[J]. 现代交通技术, 2017, 14(02): 36-38+59.
YIN J, ZHANG Y F, PENG J Y. Method research of bridge structure condition assessment based on establishing the standard field from monitoring data [J]. Modern Transportation Technology, 2017, 14(02): 36-38+59.
[3] 周勇超. 地震及汽车作用下长大混凝土桥梁振动研究[D].长安大学,2014.
ZHOU Y C. Long-extension concrete bridge vibration under actions of earthquake and road vehicle [D]. CHANG'AN University, 2014.
[4] FENG D, FENG M Q, OZER E, et al. A Vision-Based Sensor for Noncontact Structural Displacement Measurement [J]. Sensors (Basel), 2015, 15(7): 16557-75.
[5] PARK J-W, LEE J-J, JUNG H-J, et al. Vision-based displacement measurement method for high-rise building structures using partitioning approach [J]. NDT & E International, 2010, 43(7): 642-7.
[6] KIM S C, KIM H K, LEE C G, et al. A vision system for identifying structural vibration in civil engineering constructions; proceedings of the 2006 SICE-ICASE International Joint Conference, F, 2006 [C]
[7] BLACK M J, ANANDAN P. The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields [J]. Computer Vision and Image Understanding, 1996, 63(1): 75-104.
[8] BUSCA G, CIGADA A, MAZZOLENI P, et al. Vibration Monitoring of Multiple Bridge Points by Means of a Unique Vision-Based Measuring System [J]. Experimental Mechanics, 2013, 54(2): 255-71.
[9] BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters; proceedings of the 2010 IEEE computer society conference on computer vision and pattern recognition, June 13-18, 2010[C]. San Francisco: IEEE, 2010.
[10] HENRIQUES J F, CASEIRO R, MARTINS P, et al. Exploiting the circulant structure of tracking-by-detection with kernels; proceedings of the European conference on computer vision, October 7-13, 2012[C]. Florence: Springer, 2012.
[11] HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-Speed Tracking with Kernelized Correlation Filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596.
[12] DU B, SUN Y J, CAI S H, et al. Object Tracking in Satellite Videos by Fusing the Kernel Correlation Filter and the Three-Frame-Difference Algorithm[J]. Ieee Geosci Remote S, 2018, 15(2): 168-172.
[13] SHAO J, DU B, WU C, et al. Tracking Objects From Satellite Videos: A Velocity Feature Based Correlation Filter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(10): 7860-7871.
[14] ZHAO J, BAO Y Q, GUAN Z G, et al. Video-based multiscale identification approach for tower vibration of a cable-stayed bridge model under earthquake ground motions[J]. Structural Control & Health Monitoring, 2019, 26(3): e2314.
[15] CHEN X Q, XU X Q, YANG Y S, et al. Augmented Ship Tracking Under Occlusion Conditions From Maritime Surveillance Videos[J]. Ieee Access, 2020, 8: 42884-42897.
[16] BIN ZAHID F, ONG Z C, KHOO S Y. A review of operational modal analysis techniques for in-service modal identification[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2020, 42(8): 1-18.
[17] DE MOOR B, VAN OVERSCHEE P, SUYKENS J. Subspace algorithms for system identification and stochastic realization[J]. Proceedings MTNS, Kobe, Japan, 1991.
[18] 辛峻峰, 王树青, 刘福顺. 数据驱动与协方差驱动随机子空间法差异化分析[J]. 振动与冲击, 2013, 32(09): 1-4+20.
XIN J F, WANG S Q, LIU F S. Performance comparison for data-driven and covariance-driven stochastic subspace identification method [J]. Journal of Vibration and Shock, 2013, 32(09): 1-4+20.
[19] YU D H, REN W X. EMD-based stochastic subspace identification of structures from operational vibration measurements[J]. Engineering Structures, 2005, 27(12): 1741-1751.
[20] YANG Y B, CHEN W-F. Extraction of Bridge Frequencies from a Moving Test Vehicle by Stochastic Subspace Identification[J]. Journal of Bridge Engineering, 2016, 21(3): 04015053.
[21] LI W, VIET-HUNG V, LIU Z, et al. Extraction of modal parameters for identification of time-varying systems using data-driven stochastic subspace identification[J]. Journal of Vibration and Control, 2018, 24(20): 4781-4796.
[22] ZHOU X, CAO L, KHAN I, et al. Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification[J]. Shock and Vibration, 2018, 2018: 1-11.
[23] YANG X-M, YI T-H, QU C-X, et al. Performance Assessment of a High-Speed Railway Bridge through Operational Modal Analysis[J]. Journal of Performance of Constructed Facilities, 2021, 35(6): 04021091.
[24] WEN P, KHAN I, HE J, et al. Application of Improved Combined Deterministic-Stochastic Subspace Algorithm in Bridge Modal Parameter Identification[J]. Shock and Vibration, 2021, 2021: 1-11.
[25] 蔡玮,代红超,王靖,等.基于KCF算法的带屈曲约束支撑双柱式桥墩振动位移识别[J/OL].工程力学,1-15[2024-06-03].http://kns.cnki.net/kcms/detail/11.2595.O3.20231124.1119.004.html.
CAI W, DAI H C, WANG J, et al. Vibration displacement identification of double-column pier with BRBS based on KCF algorithm [J/OL]. Engineering Mechanics, 1-15 [2024-06-03]. http://kns.cnki.net/kcms/detail/11.2595.O3.20231124.1119.004.html.
[26] SCHöLKOPF B, SMOLA A J. Learning with Kernels[M]. Cambridge,MA:The MIT Press, 2018.
[27] RIFKIN R, YEO G, POGGIO T. Regularized least-squares classification[J]. Nato Science Series Sub Series III Computer and Systems Sciences, 2003, 190: 131-154.
[28] 刘志强. 基于核相关滤波的高速目标跟踪算法研究与系统实现[D].西安电子科技大学,2017.
LIU Z Q. High-speed tracking with kernelized correlation filters algorithm research and system implementation [D]. XI DIAN University, 2017.
[29] 张小宁. 基于随机子空间方法的结构模态参数自动识别[D]. 哈尔滨工业大学, 2012.
ZHANG X N. Stochastic subspace method for automotive identification of structural modal parameters [D]. Harbin Institute of Technology, 2012.