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

Abstract Identifying the local nonlinear hysteretic behaviors in structures arises as an important issue in structural health monitoring. This paper develops a new hysteresis identification framework where rather than identifying the hysteretic parameters, the restoring forces are reconstructed so as to identify the hysteretic loops. Under this new framework, there is no need to get a priori knowledge on the hysteretic models, enabling a wider range of applications, and the involved system equation is linear with unknown restoring forces, tending to be more efficient. Then, the Kalman filter is adopted for real-time restoring force reconstruction because the Kalman filter is celebrated for its efficiency, effectiveness and reliability in linear system estimation with noisy measured data. In doing so, the restoring forces are additionally augmented as state variables and such augmentation is shown to act as the role of Tikhonov regularization, by which the proper covariance of the augmentation as the regularization parameter is selected via the L-curve method. Numerical examples are presented and an experimental test is conducted to verify the effectiveness and robustness of the proposed real-time hysteresis identification method.

[1]  Xu Han,et al.  An efficient method to reduce ill-posedness for structural dynamic load identification , 2017 .

[2]  Mayergoyz,et al.  Mathematical models of hysteresis. , 1986, Physical review letters.

[3]  Geert Lombaert,et al.  An augmented Kalman filter for force identification in structural dynamics , 2012 .

[4]  Kohei Fujita,et al.  Stiffness and Damping Identification for Asymmetric Building Frame With In-plane Flexible Floors , 2019, Front. Built Environ..

[5]  Salvatore Sessa,et al.  A class of uniaxial phenomenological models for simulating hysteretic phenomena in rate-independent mechanical systems and materials , 2018 .

[6]  Jean Lemaitre,et al.  A Course on Damage Mechanics , 1992 .

[7]  Per Christian Hansen,et al.  REGULARIZATION TOOLS: A Matlab package for analysis and solution of discrete ill-posed problems , 1994, Numerical Algorithms.

[8]  J. Liu,et al.  Identification of nonlinear hysteretic parameters by enhanced response sensitivity approach , 2017 .

[9]  Salvatore Sessa,et al.  An accurate and computationally efficient uniaxial phenomenological model for steel and fiber reinforced elastomeric bearings , 2019, Composite Structures.

[10]  Eleni Chatzi,et al.  Online correction of drift in structural identification using artificial white noise observations and an unscented Kalman Filter , 2015 .

[11]  Kohei Fujita,et al.  Experimental Study on Influence of Hardening of Isolator in Multiple Isolation Building , 2016, Front. Built Environ..

[12]  Shirley J. Dyke,et al.  Real-Time Dynamic Model Updating of a Hysteretic Structural System , 2014 .

[13]  Andrew W. Smyth,et al.  On the observability and identifiability of nonlinear structural and mechanical systems , 2015 .

[14]  Jean-Louis Chaboche,et al.  A review of some plasticity and viscoplasticity constitutive theories , 2008 .

[15]  C. Papadimitriou,et al.  A dual Kalman filter approach for state estimation via output-only acceleration measurements , 2015 .

[16]  G. I N Rozvany.  Recent controversies and new challenges in structural topology optimization , 2013 .

[17]  G. Marano,et al.  Genetic-Algorithm-Based Strategies for Dynamic Identification of Nonlinear Systems with Noise-Corrupted Response , 2010 .

[18]  Joel P. Conte,et al.  Material Parameter Identification in Distributed Plasticity FE Models of Frame-Type Structures Using Nonlinear Stochastic Filtering , 2015 .

[19]  Edward J. Berger,et al.  Friction modeling for dynamic system simulation , 2002 .

[20]  Eleni Chatzi,et al.  Particle filter scheme with mutation for the estimation of time‐invariant parameters in structural health monitoring applications , 2013 .

[21]  Wei-Der Chang,et al.  An improved real-coded genetic algorithm for parameters estimation of nonlinear systems , 2006 .

[22]  Dionisio Bernal,et al.  Kalman filter damage detection in the presence of changing process and measurement noise , 2013 .

[23]  Zhong-Rong Lu,et al.  Bandlimited force identification based on sinc-dictionaries and Tikhonov regularization , 2020 .

[24]  Ian D. Aiken,et al.  AN ANALYTICAL HYSTERESIS MODEL FOR ELASTOMERIC SEISMIC ISOLATION BEARINGS , 1997 .

[25]  Costas Papadimitriou,et al.  Input-state-parameter estimation of structural systems from limited output information , 2019, Mechanical Systems and Signal Processing.

[26]  J. Liu,et al.  Parameter identification of nonlinear fractional-order systems by enhanced response sensitivity approach , 2018, Nonlinear Dynamics.

[27]  Basil Kolias,et al.  Restoring capability of bilinear hysteretic seismic isolation systems , 2008 .

[28]  Izuru Takewaki,et al.  Model-Free Identification of Hysteretic Restoring-Force Characteristic of Multi-Plane and Multi-Story Frame Model With In-Plane Flexible Floor , 2020, Frontiers in Built Environment.

[29]  James L. Beck,et al.  Real-time Reliability Estimation for Serviceability Limit States in Structures with Uncertain Dynamic Excitation and Incomplete Output Data , 2007 .

[30]  Kohei Fujita,et al.  Damage identification for frame structures using vision-based measurement , 2019 .

[31]  Yi-Qing Ni,et al.  IDENTIFICATION OF NON-LINEAR HYSTERETIC ISOLATORS FROM PERIODIC VIBRATION TESTS , 1998 .

[32]  Gaetano Russo,et al.  A new bi-linear constitutive shear relationship for Unbonded Fiber-Reinforced Elastomeric Isolators (U-FREIs) , 2017 .

[33]  Wim Desmet,et al.  Stable force identification in structural dynamics using Kalman filtering and dummy-measurements , 2015 .

[34]  Peng Pan,et al.  BASE-ISOLATION DESIGN PRACTICE IN JAPAN: INTRODUCTION TO THE POST-KOBE APPROACH , 2005 .

[35]  Hiroyuki Fujiwara,et al.  STRONG-MOTION SEISMOGRAPH NETWORK OPERATED BY NIED: K-NET AND KiK-net , 2004 .

[36]  Jeffrey Humpherys,et al.  A Fresh Look at the Kalman Filter , 2012, SIAM Rev..

[37]  E. Jacquelin,et al.  Force reconstruction: analysis and regularization of a deconvolution problem , 2003 .

[38]  Mohammed Ismail,et al.  The Hysteresis Bouc-Wen Model, a Survey , 2009 .

[39]  Joel P. Conte,et al.  Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation , 2017 .

[40]  Y. Kitada.  Identification of Nonlinear Structural Dynamic Systems Using Wavelets , 1998 .

[41]  W. Desmet,et al.  An online coupled state/input/parameter estimation approach for structural dynamics , 2015 .

[42]  G. De Roeck,et al.  Validation of joint input-state estimation for force identification and response estimation in structural dynamics , 2016 .