Tracking of Structural Dynamic Characteristics Using Recursive Stochastic Subspace Identification and Instrumental Variable Technique

Li, Zongjin; Chang, Chin-Chen

doi:10.1061/(asce)em.1943-7889.0000370

Cited by 16 publications

(6 citation statements)

References 2 publications

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“…SSI algorithms in general present high immunity to noise in the signals [65], but are computationally intensive, requiring significant computational resources especially for large structures [67,68]. Furthermore, they cannot estimate closely-spaced modes accurately because they require setting the order or number of modes which can produce an underdetermined or overdetermined system.…”

Section: Subspace Methodsmentioning

confidence: 99%

“…Further, they propose a similarity index to eliminate the fictitious modes. Li and Chang [67] introduce an optimization scheme for the online operation of the SSI-COV method and test it using the ASCE benchmark frame structure. In this algorithm, QR-factorization is substituted with the Householder bi-iteration subspace tracker, which along the utilization of a state-space constructed subspace, minimizes the required time to compute the modal parameters since only part of the measured signal needs to be used.…”

Section: Subspace Methodsmentioning

confidence: 99%

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Time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals

Perez-Ramirez¹,

Amezquita-Sanchez²,

Adeli³

et al. 2016

J. vibroeng.

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A major trust of modal parameters identification (MPI) research in recent years has been based on using artificial and natural vibrations sources because vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. This paper presents a state-of-the-art review of the time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals as well as the factors that affect the estimation accuracy. Further, the latest signal processing techniques proposed since 2012 are also reviewed. These algorithms are worth being researched for MPI of large real-life structures because they provide good time-frequency resolution and noise-immunity.

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Section: Subspace Methodsmentioning

confidence: 99%

Section: Subspace Methodsmentioning

confidence: 99%

Time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals

Perez-Ramirez¹,

Amezquita-Sanchez²,

Adeli³

et al. 2016

J. vibroeng.

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show abstract

“…The system order N can be inferred from either the power spectral density (PSD) of the observed output or the singular values of the matrix L 32 or the dimension of mass m. Note that a subjective judgment is often needed to determine N especially when the output contains unknown noise. 20 As aforementioned, the row number of Hankel matrices i should be set to be larger than N. The column number of Hankel matrices j is set to s 2 2i herein, as the last sample of the ground motion cannot be obtained as seen from equation ( 17) when using all the response output data y. The row number of the output vector l should satisfy equation (19).…”

Section: Sequential Estimation Of Structural Parameters and Ground Accelerationmentioning

confidence: 99%

Output-only subspace identification of structural properties and unknown ground excitation for shear-beam buildings

Shi

Chang

2016

Advances in Mechanical Engineering

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A novel output-only subspace identification method is proposed to estimate the structural parameters of a shear-beam building under unknown ground excitation. The unique feature of the method is that both the structural parameters and the ground acceleration can be identified using only the absolute acceleration response, and no displacement and velocity responses are required. The method uses two subspace identification techniques sequentially and iteratively. The first technique extracts modal parameters of the building (natural frequencies, damping ratios, and mode shapes) using the absolute acceleration response and assumed or identified ground acceleration. These modal parameters further lead to the estimation of the building's structural parameters when the mass properties of the building are known. The second technique then estimates the ground acceleration using the absolute acceleration and the obtained structural parameters from the first technique. The two techniques are performed iteratively until the estimated structural parameters converge. A numerical example and a laboratory test are used to illustrate the proposed identification method. Results show that the estimation of structural parameters is fairly robust to the presence of measurement noise while the unknown ground acceleration is not in high frequency range. Factors influencing the performance of the technique are also studied and discussed.

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“…SSI approaches were developed for linear time-invariant (LTI) processes. However, the modal parameters of large-scale structures and infrastructure exhibit time-varying dynamic characteristics because of structural damage, nonlinear behaviors, environmental effects, and operational conditions [17][18][19][20][21]. To keep tracking the structural state, vibrationbased methods should be continuous and autonomous during operations so that the SHM system can provide timely information.…”

Section: Introductionmentioning

confidence: 99%

Tracking Modal Parameters of Structures Online Using Recursive Stochastic Subspace Identification under Ambient Excitations

Huang,

Chen,

Weng

et al. 2024

Buildings

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Continuous and autonomous system identification is an alternative to regular inspection during operations, which is essential for structural integrity management (SIM) as well as structural health monitoring (SHM). In this regard, online (or real-time) system identification techniques that have recently received considerable attention can be used to assess the current condition and performance during operations and, in the meantime, can be utilized to detect any damage or deterioration. For example, stochastic subspace identification (SSI), based on recursive formulation, has proven its capability in tracking modal parameters as well as time-variant dynamic behaviors. This study proposes the implementation of recursive SSI (RSSI) using the matrix inversion lemma to track slow time-varying parameter changes under ambient excitations. Subsequently, some investigations for practical implementation are examined and discussed. For verifying the reliability of SHM applications based on the proposed methods, two datasets measured from different experiments are exploited to identify the modal parameters reclusively. The results from both numerical simulations and experimental investigations demonstrated the effectiveness of tracking the modal parameters exhibiting time-varying dynamic characteristics under white noise excitations (or ambient excitations).

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Tracking of Structural Dynamic Characteristics Using Recursive Stochastic Subspace Identification and Instrumental Variable Technique

Cited by 16 publications

References 2 publications

Time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals

Time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals

Output-only subspace identification of structural properties and unknown ground excitation for shear-beam buildings

Tracking Modal Parameters of Structures Online Using Recursive Stochastic Subspace Identification under Ambient Excitations

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