Parametric recognition of nonlinear structural model based on VMD and DBN

MO Ye1, WANG Zuocai1,2, DING Yajie1, YUAN Ziqing1

Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (9) : 136-143.

PDF(2839 KB)
PDF(2839 KB)
Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (9) : 136-143.

Parametric recognition of nonlinear structural model based on VMD and DBN

  • MO Ye1, WANG Zuocai1,2, DING Yajie1, YUAN Ziqing1
Author information +
History +

Abstract

A nonlinear structural model parameters identification approach based on variational mode decomposition (VMD) and deep belief network (DBN) was proposed to solve the complicated optimization process in existing methods. Firstly, the instantaneous parameters of the vibration responses were identified by the VMD method and the Hilbert transform (HT). The instantaneous parameters were regarded as independent variables after principal component analysis and the nonlinear model parameters as dependent variables. The DBN was utilized for approximating the nonlinear mapping relationship between them. Finally, the identified nonlinear model parameters could be identified directly by feeding the instantaneous parameters of the measured vibration responses after principal component analysis into the well-trained DBN. The proposed method was verified by numerical simulations of two different nonlinear 2 degrees of freedoms models and a complex frame model under earthquake excitation, and a shaking table experiment of a high voltage transmission structure. Numerical and experimental results show that the proposed method has relatively high calculation efficiency and strong anti-noise ability.

Key words

nonlinear structural model / parameters identification / mode decomposition (VMD) / deep belief network (DBN) / vibration responses / instantaneous parameters

Cite this article

Download Citations
MO Ye1, WANG Zuocai1,2, DING Yajie1, YUAN Ziqing1. Parametric recognition of nonlinear structural model based on VMD and DBN[J]. Journal of Vibration and Shock, 2022, 41(9): 136-143

References

[1]  Lei Y, Huang H F, Shen W A. Update the finite element model of Canton Tower based on direct matrix updating with incomplete modal data[J]. Smart Structures and Systems, 2012, 10(4-5): 471-483.
[2]  Zhang J, Wan C, Sato T. Advanced Markov Chain Monte Carlo Approach for Finite Element Calibration under Uncertainty[J]. Computer- Aided Civil and Infrastructure Engineering, 2013, 28(7):522-530.
[3]  周林仁, 欧进萍. 斜拉桥结构模型修正的子结构方法[J]. 振动与冲击,2014, 33(19):52-58.
ZHOU Lin-ren, OU Jin-ping. A Substructure Method of Structural Model Updating for Long-Span Cable-Stayed Bridges[J]. Journal of Vibration and Shock, 2014, 33(19):52-58.
[4]  翁顺, 左越, 朱宏平,等. 基于子结构的有限元模型修正方法[J]. 振动与冲击,2017, 36(4): 99-104.
WENG Shun, ZUO Yue,ZHU Hong-ping, et al. Model updating based on a substructuring method[J]. Journal of Vibration and Shock, 2017, 36(4): 99-104.
[5]  张皓, 李东升, 李宏男. 有限元模型修正研究进展:从线性到非线性[J]. 力学进展, 2019, 49(00):542-575.
ZHANG Hao, LI Dong-sheng, LI Hong-nan. Recent progress on finite element model updating: From linearity to nonlinearity[J]. Advances in Mechanics, 2019, 49(00):542-575.
[6]  Astroza R, Nguyen L T, Nestorovic T. Finite element model updating using simulated annealing hybridized with unscented Kalman filter[J]. Computers & Structures, 2016, 177:176-191.
[7]  Song M M, Astroza R, Ebrahimian H, et al. Adaptive Kalman filters for nonlinear finite element model updating[J]. Mechanical Systems and Signal Processing, 2020, 143:106837.
[8]  Canbaloğlu G, Özgüven H N. Model updating of nonlinear structures from measured FRFs[J]. Mechanical Systems and Signal Processing, 2016, 80:282-301.
[9]  宋正华, 姜东, 曹芝腑,等. 含铰可展桁架结构非线性模型修正方法研究[J]. 振动与冲击, 2018, 37(1):21-6.
SONG Zheng-hua, JIANG Dong, CAO Zhi-fu, et al. Nonlinear model updating method for deployable trusses with joints[J]. Journal of Vibration and Shock, 2018, 37(1):21-6.
[10]  Asgarieh E, Moaveni B, Stavridis A. Nonlinear finite element model updating of an infilled frame based on identified time-varying modal parameters during an earthquake[J]. Journal of Sound and Vibration, 2014, 333(23):6057-6073.
[11]  Wang Z C, Xin Y, Ren W X. Nonlinear structural model updating based on instantaneous frequencies and amplitudes of the decomposed dynamic responses[J]. Engineering Structures, 2015, 100:189-200.
[12]  Xie S L, Zhang Y H, Chen C H, et al. Identification of nonlinear hysteretic systems by artificial neural network[J]. Mechanical Systems and Signal Processing, 2013, 34(1-2):76-87.
[13]  Hasancebi O, Dumlupinar T. Linear and nonlinear model updating of reinforced concrete T-beam bridges using artificial neural networks [J]. Computers & Structures, 2013, 119:1-11.
[14]  Dragomiretskiy K, Zosso D. Variational Mode Decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3):531-544.
[15]  Ni P H, Li J, Hao H, et al. Time-varying system identification using variational mode decomposition[J]. Structural Control and Health Monitoring, 2018, 25(6):20.
[16]  Wang Z C, Ren W X, Chen G D. Time-Varying Linear and Nonlinear Structural Identification with Analytical Mode Decomposition and Hilbert Transform[J]. Journal of Structural Engineering, 2013, 139(12):06013001.
[17]  Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[18]  Mckenna F. OpenSees: A Framework for Earthquake Engineering Simulation[J]. Computing in Science & Engineering, 2011, 13(4):58-66.
[19]  Gupta A, Krawinkler H. Behavior of Ductile SMRFs at Various Seismic Hazard Levels[J]. Journal of Structural Engineering, 2000, 126(1):98-107.
[20]  Wang Z C , Ren W X , Chen G D . Time-varying system identification of high voltage switches of a power substation with slide-window least-squares parameter estimations[J]. Smart Materials and Structures, 2013, 22(6):065023.
PDF(2839 KB)

276

Accesses

0

Citation

Detail

Sections
Recommended

/