Adaptive
 
 H
 
 ∞
 Filter: Its Application to Structural Identification

Sato, Tadanobu; Qi, Kai

doi:10.1061/(asce)0733-9399(1998)124:11(1233)

Cited by 101 publications

(7 citation statements)

References 8 publications

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“…The results might not be immediately available after an event, especially if human input is required. Adaptive H∞ filter techniques (Sato and Qi 1998) and Kalman filter methods (Lee and Yun 2008, Loh et al 2000, Yang et al 2006 can achieve real-time or rapid results. However, they have significant computational cost and complexity, and are better for linear systems.…”

Section: Introductionmentioning

confidence: 99%

Overall damage identification of flag-shaped hysteresis systems under seismic excitation

Zhou¹,

Chase²,

Rodgers³

et al. 2015

Smart Structures and Systems

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This research investigates the structural health monitoring of nonlinear structures after a major seismic event. It considers the identification of flag-shaped or pinched hysteresis behavior in response to structures as a more general case of a normal hysteresis curve without pinching. The method is based on the overall least squares methods and the log likelihood ratio test. In particular, the structural response is divided into different loading and unloading sub-half cycles. The overall least squares analysis is first implemented to obtain the minimum residual mean square estimates of structural parameters for each sub-half cycle with the number of segments assumed. The log likelihood ratio test is used to assess the likelihood of these nonlinear segments being true representations in the presence of noise and model error. The resulting regression coefficients for identified segmented regression models are finally used to obtain stiffness, yielding deformation and energy dissipation parameters. The performance of the method is illustrated using a single degree of freedom system and a suite of 20 earthquake records. RMS noise of 5%, 10%, 15% and 20% is added to the response data to assess the robustness of the identification routine. The proposed method is computationally efficient and accurate in identifying the damage parameters within 10% average of the known values even with 20% added noise. The method requires no user input and could thus be automated and performed in real-time for each sub-half cycle, with results available effectively immediately after an event as well as during an event, if required.

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Section: Introductionmentioning

confidence: 99%

Overall damage identification of flag-shaped hysteresis systems under seismic excitation

Zhou¹,

Chase²,

Rodgers³

et al. 2015

Smart Structures and Systems

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show abstract

“…( 14) and compare the capability of PhyLSTM 2 and PhyLSTM 3 for complex hysteresis modeling. The Bouc-Wen model [52,62] is adopted for showcase, in which, for the ith degree-of-freedom (DOF), the rate-dependent hysteresis is expressed as [63]:…”

Section: Numerical Validation: Bouc-wen Hysteresis Modelmentioning

confidence: 99%

Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures

Zhang,

Liu,

Sun

2020

Preprint

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This paper introduces an innovative physics-informed deep learning framework for metamodeling of nonlinear structural systems with scarce data. The basic concept is to incorporate physics knowledge (e.g., laws of physics, scientific principles) into deep long short-term memory (LSTM) networks, which boosts the learning within a feasible solution space. The physics constraints are embedded in the loss function to enforce the model training which can accurately capture latent system nonlinearity even with very limited available training datasets. Specifically for dynamic structures, physical laws of equation of motion, state dependency and hysteretic constitutive relationship are considered to construct the physics loss. In particular, two physics-informed multi-LSTM network architectures are proposed for structural metamodeling. The satisfactory performance of the proposed framework is successfully demonstrated through two illustrative examples (e.g., nonlinear structures subjected to ground motion excitation). It turns out that the embedded physics can alleviate overfitting issues, reduce the need of big training datasets, and improve the robustness of the trained model for more reliable prediction. As a result, the physics-informed deep learning paradigm outperforms classical non-physics-guided data-driven neural networks.

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“…Most algorithms can be categorized into two groups, that is, time-domain and frequency-domain approaches. Time-domain approaches deal with time history data collected from the actual structure directly, without the requirement for extracting modal properties (Chatzi and Smyth, 2009; Doucet and Tadić, 2003; Sato and Qi, 1998; Yang et al, 2006). Overall, the time-domain approaches suffer convergence difficulties and high computational cost when applied to large-scale FE models.…”

Section: Introductionmentioning

confidence: 99%

Formulation and application of SMU: An open-source MATLAB package for structural model updating

Otsuki

Lander

Dong

et al. 2022

Advances in Structural Engineering

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This paper describes a MATLAB package for structural model updating, named SMU. The SMU package updates parameter values of a finite element model by solving optimization problems utilizing modal properties obtained from sensor measurements. In particular, the package offers three model updating formulations, namely, (1) the modal assurance criterion value approach, (2) the eigenvector difference approach, and (3) the modal dynamic residual formulation. The first two belong to the family of modal property difference formulations. For each formulation, the analytical Jacobian derivative of the objective function is derived and implemented in SMU. Since the formulated optimization problems are generally nonconvex, the global optimality of the solution cannot be guaranteed using off-the-shelf optimization algorithms. In order to increase the chance of finding a better local minimum, the SMU package can perform gradient search from randomly generated starting points. Several examples for the model updating of as-built structures are included in the GitHub package. This paper demonstrates the SMU functionality through model updating of an 18-DOF model and a concrete building frame model.

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Adaptive H _∞ Filter: Its Application to Structural Identification

Cited by 101 publications

References 8 publications

Overall damage identification of flag-shaped hysteresis systems under seismic excitation

Overall damage identification of flag-shaped hysteresis systems under seismic excitation

Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures

Formulation and application of SMU: An open-source MATLAB package for structural model updating

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Adaptive H ∞ Filter: Its Application to Structural Identification

Cited by 101 publications

References 8 publications

Overall damage identification of flag-shaped hysteresis systems under seismic excitation

Overall damage identification of flag-shaped hysteresis systems under seismic excitation

Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures

Formulation and application of SMU: An open-source MATLAB package for structural model updating

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Adaptive H _∞ Filter: Its Application to Structural Identification