Real-time hysteresis identification in structures based on restoring force reconstruction and Kalman filter

Wang, Li; Guo, Jiqing; Takewaki, Izuru

doi:10.1016/j.ymssp.2020.107297

Cited by 19 publications

(16 citation statements)

References 42 publications

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“…Since the exponential integrator in Equation ( 12) has been studied extensively with high accuracy in many applications, 19 and the effectiveness of the zero order assumption for 𝒛 and 𝒑 in seismic analysis has been validated in, 5 we use this classic exponential time-step scheme for the integration of the linear part of the differential equation in Equation (9a).…”

Section: Physics-dnn Hybridized Integration Time-steppermentioning

confidence: 99%

“…This benchmark numerical example can also be found in other references (cf. 5,18 ). Properties of this model are: story mass 𝑚 = 2 × 10 3 kg, inter-story elastic stiffness 𝑘 = 1 × 10 6 N/m, and 1% inherent damping ratio of the primary structure.…”

Section: Numerical Investigationmentioning

confidence: 99%

“…spaces is a main beneficiary of the this convergence and some representative techniques include Kalman filter, 5 Bayesian inference, 6 Fuzzy theory, 7 and others. In similar fashion, current practices on discovering dynamics systems using observational data through deep learning (DL) and artificial intelligence (AI) algorithms alleviate the need of physical model design and have emerged as a new field in structural dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Combination of physics‐based and data‐driven modeling for nonlinear structural seismic response prediction through deep residual learning

Guo

Enokida

et al. 2023

Earthq Engng Struct Dyn

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Despite great progress in seeking accurate numerical approximator to nonlinear structural seismic response prediction using deep learning approaches, tedious training process and large volume of structural response data under earthquakes for training and validation are often prohibitively accessible. In our methodology, the main innovation can be seen in the incorporation of deep neural networks (DNNs) into a classical numerical integration method by using a hybridized integration time-stepper. In this way, the linear physics information of the structure and the obscure nonlinear dynamics are smoothly combined. We propose to use residual network (ResNet) to learn time-stepping schemes specifically for the nonlinear state variables of the system. Our Physics-DNN hybridized integration (PDHI) time-stepping scheme provides important advantages over current pure data-driven approaches, including (i) a flexible framework incorporating known time-invariant physics information, (ii) requirement of structural seismic response data being circumvented by simple short bursts of trajectories collected from underlying nonlinear components, and (iii) efficiency in training and validation process. Besides, our results indicate that a simple feedforward or convolutional architecture outperforms recurrent networks to fulfill the requirement of prediction accuracy as well as long-range memory in structural dynamic analysis. Several numerical and experimental examples are presented to demonstrate the performance of the method.

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Section: Physics-dnn Hybridized Integration Time-steppermentioning

confidence: 99%

Section: Numerical Investigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combination of physics‐based and data‐driven modeling for nonlinear structural seismic response prediction through deep residual learning

Guo

Enokida

et al. 2023

Earthq Engng Struct Dyn

Self Cite

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“…Remark 1. Hysteresis is a common memory nonlinearity in practical systems [54]- [56]. However, hysteresis is a destructive nonlinearity that leads to oscillation, lag and even instability in practical systems [57].…”

Section: Problem Descriptionmentioning

confidence: 99%

A New Robust Identification Algorithm for Hammerstein-Like System Using Identification Error Structure

Wang

2022

IEEE Access

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A new robust identification algorithm is introduced in this study for a Hammersteinlike system based on identification error information. With the help of the half-substitution idea, the identification model is converted into a compact model where the coupling parameters are avoided. To reduce the effect of noise signals, a filter gain is proposed to obtain helpful system data. Then, on the basis of the filtered variables and developed forcing variables, the identification error information is extracted from the helpful system data. By using the identification error data, a new parameter estimation adaptive law that differs from the classic prediction error method is derived. Therefore, a new identification scheme framework is proposed and the weakness of the prediction error method is improved. Simulations and a real-life plant are presented to test the validity and practicality of the presented identification approach.The results of parameter estimation and estimation error qualitatively demonstrated the advantages of the proposed algorithm. The computational complexity and performance evaluation indicators results quantitatively indicated that the proposed algorithm produces higher estimation performance compared with the other algorithms.

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“…A nonparametric hysteretic behavior and mass identification approach was presented by Xu et al 42 using the EKF with weighted global iteration (EKF‐WGI) for multi‐degree‐of‐freedom (MDOF) structures where structural nonlinear behavior is modeled with a power polynomial model. Wang et al 43 presented real‐time nonparametric hysteresis identification approach based on restoring force reconstruction and the Kalman filter (KF). Numerical study and an experimental test are conducted to verify the effectiveness and robustness of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Hysteresis and dynamic loading nonparametric identification for multi‐degree‐of‐freedom structures using an updated general extended Kalman filter and a Legendre polynomial model

Zhao

Deng

et al. 2022

Structural Contr & Hlth

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In order to identify the hysteretic behavior in the form of nonlinear restoring force (NRF) and the unknown dynamic excitation when the acceleration measurement at the degree of freedom (DOF) of the excitation is unknown, and considering the fact that it is difficult to establish a general parametric mathematical model in advance to describe the real hysteretic behavior of an engineering structure, in this paper, a nonparametric identification approach for both NRF and dynamic loading is presented using an updated general extended Kalman filter with unknown input (UGEKF-UI) algorithm with lim-

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Real-time hysteresis identification in structures based on restoring force reconstruction and Kalman filter

Cited by 19 publications

References 42 publications

Combination of physics‐based and data‐driven modeling for nonlinear structural seismic response prediction through deep residual learning

Combination of physics‐based and data‐driven modeling for nonlinear structural seismic response prediction through deep residual learning

A New Robust Identification Algorithm for Hammerstein-Like System Using Identification Error Structure

Hysteresis and dynamic loading nonparametric identification for multi‐degree‐of‐freedom structures using an updated general extended Kalman filter and a Legendre polynomial model

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