Comparison of restoring force models for the identification of structures with hysteresis and degradation

Abstract When subjected to events such as earthquakes, engineering structures typically exhibit a nonlinear and hysteretic behaviour with stiffness and strength degradations. Though a reliable evaluation of safety conditions should take into account the nonlinear dynamic and evolutionary nature of the structural response, the experimental identification of a nonlinear behaviour under dynamic and seismic loading is, to date, an open problem. The present research aims at evaluating the potential of different restoring force models for simulating the seismic response of hysteretic structural systems, with special emphasis on the two main problems encountered when applying this approach to full-scale structures under intense excitation: (a) a markedly time-dependent behaviour; (b) need to compare among different restoring force models, either expressed in a parametric or polynomial form. In particular, polynomial models will be formulated both in terms of restoring force and its derivative, in order to present a comprehensive discussion of different strategies. The nonlinear identification technique employed in this paper is required to account for a time-dependent behaviour. In fact, in presence of degradation or any other time-varying characteristics, instantaneous identification certainly constitutes an enhancement of the classical restoring force based approach, and may as well provide checks on the consistency of the assumed models.

[1]  Edward F. Crawley,et al.  Identification of nonlinear structural elements by force-state mapping , 1984 .

[2]  Wang Feng-quan,et al.  Modal identification based on Gaussian continuous time autoregressive moving average model , 2010 .

[3]  Yongmin Yang,et al.  Parameter identification of inelastic structures under dynamic loads , 2002 .

[4]  K. Worden,et al.  Data processing and experiment design for the restoring force surface method, part II: Choice of excitation signal , 1990 .

[5]  Johan Schoukens,et al.  A LOCAL RESTORING FORCE SURFACE METHoD , 2002 .

[6]  M. B. Priestley,et al.  Power spectral analysis of non-stationary random processes , 1967 .

[7]  Sami F. Masri,et al.  Identification and modeling of nonlinear systems , 1982 .

[8]  S. Masri,et al.  Identification of the state equation in complex non-linear systems , 2004 .

[9]  M. A. Al-Hadid,et al.  Developments in the force-state mapping technique for non-linear systems and the extension to the location of non-linear elements in a lumped-parameter system , 1989 .

[10]  Sami F. Masri,et al.  A Nonparametric Identification Technique for Nonlinear Dynamic Problems , 1979 .

[11]  Rosario Ceravolo,et al.  Instantaneous Identification of Degrading Hysteretic Oscillators Under Earthquake Excitation , 2010 .

[12]  Timothy P. Waters,et al.  Signal processing for experimental modal analysis , 2001, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[13]  Jann N. Yang,et al.  On-line identification of non-linear hysteretic structures using an adaptive tracking technique , 2004 .

[14]  Fabrizio Vestroni,et al.  IDENTIFICATION OF HYSTERETIC OSCILLATORS UNDER EARTHQUAKE LOADING BY NONPARAMETRIC MODELS , 1995 .

[15]  Chung Bang Yun,et al.  Identification of Linear Structural Dynamic Systems , 1982 .

[16]  José Rodellar,et al.  Dynamic properties of the hysteretic Bouc-Wen model , 2007, Syst. Control. Lett..

[17]  Silvano Erlicher,et al.  Pseudopotentials and Loading Surfaces for an Endochronic Plasticity Theory with Isotropic Damage , 2008 .

[18]  Yoshiyuki Suzuki,et al.  Improvement Of Parameter Estimation for Non-Linear Hysteretic Systems With Slip By A Fast Bayesian Bootstrap Filter , 2004 .

[19]  Rosario Ceravolo,et al.  Time–Frequency Analysis , 2009 .

[20]  Yoshiyuki Suzuki,et al.  Identification of non-linear hysteretic systems with slip , 2004 .

[21]  Spilios D. Fassois,et al.  Parametric time-domain methods for non-stationary random vibration modelling and analysis — A critical survey and comparison , 2006 .

[22]  Jiuchao Feng,et al.  Real-time nonlinear structural system identification via iterated unscented Kalman filter , 2012 .

[23]  Oreste S. Bursi,et al.  Bouc–Wen-Type Models with Stiffness Degradation: Thermodynamic Analysis and Applications , 2008 .

[24]  George Finlay Simmons,et al.  Introduction to Topology and Modern Analysis , 1963 .

[25]  A. Visintin Differential models of hysteresis , 1994 .

[26]  Andrew W. Smyth,et al.  On-Line Parametric Identification of MDOF Nonlinear Hysteretic Systems , 1999 .

[27]  Roger Ghanem,et al.  Structural System Identification. II: Experimental Verification , 1995 .

[28]  Thomas T. Baber,et al.  Random Vibration Hysteretic, Degrading Systems , 1981 .

[29]  Rosario Ceravolo,et al.  Instantaneous Identification of Bouc-Wen-Type Hysteretic Systems from Seismic Response Data , 2007 .

[30]  Paul R. White,et al.  THE ANALYSIS OF NON-STATIONARY SIGNALS USING TIME-FREQUENCY METHODS , 1996 .

[31]  G. Tomlinson,et al.  Nonlinearity in Structural Dynamics: Detection, Identification and Modelling , 2000 .

[32]  Y. Wen Method for Random Vibration of Hysteretic Systems , 1976 .

[33]  Andrew W. Smyth,et al.  Real-time parameter estimation for degrading and pinching hysteretic models , 2008 .

[34]  Tudor Sireteanu,et al.  Identification of an extended Bouc–Wen model with application to seismic protection through hysteretic devices , 2010 .

[35]  Kai Qi,et al.  Adaptive H ∞ Filter: Its Application to Structural Identification , 1998 .

[36]  Li Zhou,et al.  An adaptive extended Kalman filter for structural damage identification , 2006 .

[37]  V. K. Koumousis,et al.  Identification of Bouc-Wen hysteretic systems by a hybrid evolutionary algorithm , 2008 .

[38]  Silvano Erlicher,et al.  Thermodynamic admissibility of Bouc-Wen type hysteresis models , 2004 .

[39]  Yoshiyuki Suzuki,et al.  Identification of hysteretic systems with slip using bootstrap filter , 2004 .

[40]  Bruno Torrésani,et al.  Practical Time-Frequency Analysis , 1998 .

[41]  Virginia Torczon,et al.  On the Convergence of Pattern Search Algorithms , 1997, SIAM J. Optim..

[42]  Silvano Erlicher,et al.  Endochronic theory, non-linear kinematic hardening rule and generalized plasticity : a new interpretation based on generalized normality assumption , 2006, 0812.1884.

[43]  Charles R. Farrar,et al.  An experimental investigation of change detection in uncertain chain-like systems , 2010 .

[44]  E. F. Crawley,et al.  Identification of nonlinear system parameters in joints using the force-state mapping technique , 1986 .

[45]  K. Worden,et al.  Data processing and experiment design for the restoring force surface method, part I: integration and differentiation of measured time data , 1990 .

[46]  R. Ceravolo Use of instantaneous estimators for the evaluation of structural damping , 2004 .

[47]  Spilios D. Fassois,et al.  Output-only identification and dynamic analysis of time-varying mechanical structures under random excitation: A comparative assessment of parametric methods , 2010 .

[48]  K. Worden,et al.  Past, present and future of nonlinear system identification in structural dynamics , 2006 .