A dynamic stiffness-based framework for harmonic input estimation and response reconstruction considering damage

Yixian LI , Limin SUN , Wang ZHU , Wei ZHANG

Front. Struct. Civ. Eng. ›› 2022, Vol. 16 ›› Issue (4) : 448 -460.

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Front. Struct. Civ. Eng. ›› 2022, Vol. 16 ›› Issue (4) : 448 -460. DOI: 10.1007/s11709-022-0805-5
RESEARCH ARTICLE
RESEARCH ARTICLE

A dynamic stiffness-based framework for harmonic input estimation and response reconstruction considering damage

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Abstract

In structural health monitoring (SHM), the measurement is point-wise but structures are continuous. Thus, input estimation has become a hot research subject with which the full-field structural response can be calculated with a finite element model (FEM). This paper proposes a framework based on the dynamic stiffness theory, to estimate harmonic input, reconstruct responses, and to localize damages from seriously deficient measurements. To begin, Fourier transform converts the dynamic equilibrium equation to an equivalent static one in the frequency domain, which is under-determined since the dimension of measurement vector is far less than the FEM-node number. The principal component analysis has been adopted to “compress” the under-determined equation, and formed an over-determined equation to estimate the unknown input. Then, inverse Fourier transform converts the estimated input in the frequency domain to the time domain. Applying this to the FEM can reconstruct the target responses. If a structure is damaged, the estimated nodal force can localize the damage. To improve the damage-detection accuracy, a multi-measurement-based indicator has been proposed. Numerical simulations have validated that the proposed framework can capably estimate input and reconstruct multi-types of full-field responses, and the damage indicator can localize minor damages even with the existence of noise.

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Keywords

dynamic stiffness / principal component analysis / response reconstruction / damage localization / under-determined equation

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Yixian LI, Limin SUN, Wang ZHU, Wei ZHANG. A dynamic stiffness-based framework for harmonic input estimation and response reconstruction considering damage. Front. Struct. Civ. Eng., 2022, 16(4): 448-460 DOI:10.1007/s11709-022-0805-5

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References

[1]

Yang C Q, Yang D, He Y, Wu Z S, Xia Y F, Zhang Y F. Moving load identification of small and medium-sized bridges based on distributed optical fiber sensing. International Journal of Structural Stability and Dynamics, 2016, 16( 4): 1640021

[2]

Lydon M, Taylor S E, Robinson D, Mufti A, Brien E J O. Recent developments in bridge weigh in motion (B-WIM). Journal of Civil Structural Health Monitoring, 2016, 6( 1): 69–81

[3]

Yu Y, Cai C S, Deng L. State-of-the-art review on bridge weigh-in-motion technology. Advances in Structural Engineering, 2016, 19( 9): 1514–1530

[4]

Bao T, Babanajad S K, Taylor T, Ansari F. Generalized method and monitoring technique for shear-strain-based bridge weigh-in-motion. Journal of Bridge Engineering, 2016, 21( 1): 04015029

[5]

Lansdell A, Song W, Dixon B. Development and testing of a bridge weigh-in-motion method considering nonconstant vehicle speed. Engineering Structures, 2017, 152 : 709–726

[6]

Zhao H, Uddin N, O’Brien E J, Shao X, Zhu P. Identification of vehicular axle weights with a bridge weigh-in-motion system considering transverse distribution of wheel loads. Journal of Bridge Engineering, 2014, 19( 3): 04013008

[7]

Chan T H T, Law S S, Yung T H, Yuan X R. An interpretive method for moving force identification. Journal of Sound and Vibration, 1999, 219( 3): 503–524

[8]

Yu L, Chan T H T. Moving force identification based on the frequency–time domain method. Journal of Sound and Vibration, 2003, 261( 2): 329–349

[9]

Law S S, Chan T H, Zeng Q. Moving force identification: A time domain method. Journal of Sound and Vibration, 1997, 201( 1): 1–22

[10]

Amiri A K, Bucher C. A procedure for in situ wind load reconstruction from structural response only based on field testing data. Journal of Wind Engineering and Industrial Aerodynamics, 2017, 167 : 75–86

[11]

Kazemi Amiri A, Bucher C. Derivation of a new parametric impulse response matrix utilized for nodal wind load identification by response measurement. Journal of Sound and Vibration, 2015, 344 : 101–113

[12]

Law S S, Bu J Q, Zhu X Q. Time-varying wind load identification from structural responses. Engineering Structures, 2005, 27( 10): 1586–1598

[13]

Hwang J S, Kareem A, Kim H. Wind load identification using wind tunnel test data by inverse analysis. Journal of Wind Engineering and Industrial Aerodynamics, 2011, 99( 1): 18–26

[14]

Zhi L, Li Q S, Fang M, Yi J. Identification of wind loads on supertall buildings using Kalman filtering-based inverse method. Journal of Structural Engineering, 2017, 143( 4): 06016004

[15]

Zhi L, Fang M, Li Q S. Estimation of wind loads on a tall building by an inverse method. Structural Control and Health Monitoring, 2017, 24( 4): e1908

[16]

Li Y, Huang H, Zhang W, Sun L. Structural full-field responses reconstruction by the SVD and pseudo-inverse operator-estimated force with two-degree multi-scale models. Engineering Structures, 2021, 249 : 112986

[17]

Gillijns S, de Moor B. Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough. Automatica, 2007, 43( 5): 934–937

[18]

Fang H, de Callafon R A. On the asymptotic stability of minimum-variance unbiased input and state estimation. Automatica, 2012, 48( 12): 3183–3186

[19]

Hsieh C S. Extension of unbiased minimum-variance input and state estimation for systems with unknown inputs. Automatica, 2009, 45( 9): 2149–2153

[20]

Pan S, Xiao D, Xing S, Law S S, Du P, Li Y. A general extended Kalman filter for simultaneous estimation of system and unknown inputs. Engineering Structures, 2016, 109 : 85–98

[21]

Pan S, Su H, Wang H, Chu J. The study of joint input and state estimation with Kalman filtering. Transactions of the Institute of Measurement and Control, 2011, 33( 8): 901–918

[22]

Yong S Z, Zhu M, Frazzoli E. A unified filter for simultaneous input and state estimation of linear discrete-time stochastic systems. Automatica, 2016, 63 : 321–329

[23]

Hsieh C S, Chen F C. Optimal solution of the two-stage Kalman estimator. IEEE Transactions on Automatic Control, 1995, 44( 1): 194–199

[24]

Niu Y, Fritzen C P, Jung H, Buethe I, Ni Y Q, Wang Y W. Online simultaneous reconstruction of wind load and structural responses-theory and application to Canton Tower. Computer-Aided Civil and Infrastructure Engineering, 2015, 30( 8): 666–681

[25]

Nord T S, Lourens E M, Øiseth O, Metrikine A. Model-based force and state estimation in experimental ice-induced vibrations by means of Kalman filtering. Cold Regions Science and Technology, 2015, 111 : 13–26

[26]

Hsieh C S, Chen F C. Robust two-stage Kalman filters for systems with unknown. IEEE Transactions on Automatic Control, 2000, 45( 12): 2374–2378

[27]

Zhang C D, Xu Y L. Structural damage identification via response reconstruction under unknown excitation. Structural Control and Health Monitoring, 2017, 24( 8): e1953

[28]

Zhi L, Li Q S, Fang M. Identification of wind loads and estimation of structural responses of super-tall buildings by an inverse method. Computer-Aided Civil and Infrastructure Engineering, 2016, 31( 12): 966–982

[29]

Zhang W, Sun L M, Sun S W. Bridge-deflection estimation through inclinometer data considering structural damages. Journal of Bridge Engineering, 2017, 22( 2): 04016117

[30]

Sun L M, Zhang W, Nagarajaiah S. Bridge real-time damage identification method using inclination and strain measurements in the presence of temperature variation. Journal of Bridge Engineering, 2019, 24( 2): 04018111

[31]

Li Y, Sun L, Zhang W, Nagarajaiah S. Bridge damage detection from the equivalent damage load by multitype measurements. Structural Control and Health Monitoring, 2021, 28( 5): e2709

[32]

Bernal D. Load vectors for damage localization. Journal of Engineering Mechanics, 2002, 128( 1): 7–14

[33]

Bernal D. Damage localization from the null space of changes in the transfer matrix. AIAA Journal, 2007, 45( 2): 374–381

[34]

Wold S, Esbensen K, Geladi P. Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 1987, 2( 1–3): 37–52

[35]

JolliffeI. Principal Component Analysis. 2nd ed. New York: Springer, 2010

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