On‐line identification of non‐linear hysteretic structural systems using a variable trace approach

Lin, Jeng-Wen; Betti, Raimondo; Smyth, Andrew W.; Longman, Richard W.

doi:10.1002/eqe.63

Cited by 131 publications

(87 citation statements)

References 24 publications

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“…The number of 'intermediate' threshold values can vary and usually it is a trade-o between faster parameter convergence and stability, smoother adaptation gain readjustment, and computational time. In this study, we use three threshold marks (1, 0.158, and 0.025): the upper and lower values correspond to an equivalent variability range of the diag( G −1 k+1 ) values used in the constant trace method [19,20]. Because of the non-linear decaying behavior of the adaptation gains, the intermediate value is selected so as to maintain an approximate multiple relationship among these three values.…”

Section: −1mentioning

confidence: 99%

“…This transfer matrix is assumed to be known, either because the law of variability of the parameters with time is known, or simply because, based on the various assumptions made, it is possible to 'guess' the structure of such a matrix. A recursive least-square expression for the estimated parameter vectorÂ k+1 can be written as [19]:…”

Section: Least-square (Kalman Filter) Based Identification Algorithmmentioning

confidence: 99%

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On‐line identification and damage detection in non‐linear structural systems using a variable forgetting factor approach

Lin

Betti

2003

Earthq Engng Struct Dyn

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SUMMARYThis paper presents an adaptive on-line identiÿcation algorithm based on a newly deÿned variable forgetting factor approach. At each time step, the proposed methodology, a recursive least-square (Kalman ÿlter) based algorithm, upgrades the adaptation gain matrix using an adaptive forgetting factor that is expressed as the ratio between the minimum value of the diagonal elements of the adaptation gain matrix and a set of pre-deÿned threshold values. This approach requires only acceleration measurements and is particularly robust to the integration errors introduced in determining velocities and displacements. Such an algorithm has been implemented for both parametric and non-parametric identiÿcation studies. For the case of no a priori information on the type of the structural model, an analytical method based on a power series of multivariable polynomial expansions has been introduced. The e ectiveness and robustness of the proposed approach has been shown in various numerical simulations, considering the e ects of excitation amplitude, of measurement noise, of a priori mass estimation error, and of exact-, under-, and over-parameterization of the structural model. General polynomial-type non-linear models such as Du ng and Van der Pole oscillators and Bouc-Wen hysteretic model have been analyzed using the proposed modeling process. The methodology presented in this paper leads to a more accurate and controllable on-line identiÿcation either for the estimation of time-invariant or time-varying structural parameters (for damage detection purposes) or for the prediction of the future structural response to dynamic loading.

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Section: −1mentioning

confidence: 99%

Section: Least-square (Kalman Filter) Based Identification Algorithmmentioning

confidence: 99%

On‐line identification and damage detection in non‐linear structural systems using a variable forgetting factor approach

Lin

Betti

2003

Earthq Engng Struct Dyn

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“…In the past decades, efforts have been devoted to developing the identification procedures for non-linear hysteretic systems [14][15][16][17]. But most of them were presented by referring to the BoucWen model and other linear hysteretic models due to their significant advantages.…”

Section: *Corresponding Authormentioning

confidence: 99%

Identification of hysteretic systems with slip using bootstrap filter

Suzuki

Noori

2004

Mechanical Systems and Signal Processing

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Hysteretic models with slip are frequently used to predict the non-linear behaviour of many structural systems, for example wood buildings and reinforced concrete structures. A model, called SL model, which can describe the pinching of most practical hysteresis loops perfectly was proposed by Baber and Noori. This model is characterised by control parameters that have to be identified from observed experimental data. A method of estimating the parameters of SL model on the basis of input-output data based on Bayesian state estimation and bootstrap filter is suggested in this paper, which has the great advantages of being able to handle any functional non-linearity and system and measurement noise of any distribution. A numerical simulation shows its suitability and effective for the system even in the case of very severe material non-linearity.

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“…However, they have significant computational cost and complexity. Simpler and more suitable algorithms for on-line SHM make use of Least Squares Estimation (LSE) [3,[9][10][11][12][13][14] with different stochastic gradient estimation approaches.…”

Section: Introductionmentioning

confidence: 99%

LMS-based approach to structural health monitoring of nonlinear hysteretic structures

Nayyerloo

Chase

MacRae

et al. 2010

Structural Health Monitoring

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Structural health monitoring (SHM) algorithms based on adaptive least mean squares (LMS) filtering theory can directly identify time-varying changes in structural stiffness in real-time in a computationally efficient fashion. However, better metrics of seismic structural damage and future utility after an event are related to permanent and total plastic deformations. This study presents a modified LMS-based SHM method and a novel two-step structural identification technique using a baseline nonlinear Bouc—Wen structural model to directly identify changes in stiffness due to damage as well as plastic or permanent deflections. The algorithm is designed to be computationally efficient; therefore it can work in real-time. An in silico single-degree-of-freedom (SDOF) nonlinear shear-type structure is used to prove the concept. The efficiency of the proposed SHM algorithm in identifying stiffness changes and plastic/permanent deflections is assessed under different ground motions using a suite of 20 different ground acceleration records. The results show that in a realistic scenario with fixed filter tuning parameters, the proposed LMS-based SHM algorithm identifies stiffness changes to within 10% of true values within 2s. Permanent deflection is identified to within 14% of the actual as-modeled value using noise-free simulation-derived structural responses. This latter value provides important post-event information on the future serviceability, safety, and repair cost.

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On‐line identification of non‐linear hysteretic structural systems using a variable trace approach

Cited by 131 publications

References 24 publications

On‐line identification and damage detection in non‐linear structural systems using a variable forgetting factor approach

On‐line identification and damage detection in non‐linear structural systems using a variable forgetting factor approach

Identification of hysteretic systems with slip using bootstrap filter

LMS-based approach to structural health monitoring of nonlinear hysteretic structures

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