On-Line Parametric Identification of MDOF Nonlinear Hysteretic Systems

Smyth, Andrew W.; Masri, Sami F.; Chassiakos, Anastasios; Caughey, T. K.

doi:10.1061/(asce)0733-9399(1999)125:2(133)

Cited by 177 publications

(123 citation statements)

References 26 publications

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“…Hence, Equations (6)- (11) can be summarised: where n is the number of degrees of freedom of the model, and K i is the corresponding time-varying matrix to i th DOF in Equations (6)- (10). Rewriting Equation (4) using Equations (6)- (12) yields:…”

Section: A R T R T Z T Z T R T Z T Z T Ymentioning

confidence: 99%

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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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Section: A R T R T Z T Z T R T Z T Z T Ymentioning

confidence: 99%

“…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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“…Noteworthy contributions have been made by Masri and Caughey (1979); Beck and Jennings (1980); Hoshiya and Saito (1983); Masri et al (1987a, b); Ghanem and Shinozuka (1995); Shinozuka and Ghanem (1995); Qi and Sato (1999); Smyth et al (1999);and Sano et al (1999). Furukawa et al (2000) proposed a prediction error method (PEM) with a nonlin ear state-space model and carried out system identification of a base-isolated structure using a one-directional MDOF model in which the base isolation system was assumed to have a piecewise linear restoring force displacement relation.…”

Section: Introductionmentioning

confidence: 99%

System Identification of Base-Isolated Building using Seismic Response Data

et al. 2005

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Due to the complex nature of the excitation, and the inherent dynamics characteristics of restoring force of the base isolation systems, the response of base-isolated structures subject to strong earthquakes often experiences excursion into the inelastic range. Therefore, in designing base-isolated structures, the nonlinear hysteretic restoring force model of the base isolation system is frequently used to predict structural response and to evaluate structural safety. In this paper, the prediction error method system identification technique is used in conjunction with nonlinear state-space models for identification of a base-isolated structure. Using a variety of nonlinear restoring force models and bidirectional recorded seismic responses, several identification runs are conducted to evaluate the accuracy of the selected models. Several nonlinear restoring force models are utilized for the base-isolation system, including a multiple shear spring (MSS) model. Among all models used, results indicate that the trilinear hysteretic MSS model closely matches the actual hysteretic restoring force profile and time histories obtained directly from the observed data. Introd uctionThe behavior of base-isolated structures during an earthquake is highly affected by the characteristics of the base isolation system. The base isolation system separates the structure from its founda tion and primarily moves the natural frequency of the structure away from the dominant frequency range of the excitation via its low stiffness relative to that of the upper structure.Construction of base-isolated structures has increased, espe cially after the recent strong earthquakes in the United States and Japan. Despite the limited number of recorded seismic response data, vigorous studies to evaluate the actual behavior of base isolated structures during strong earthquakes have been con ducted. Nonlinearity in structural response is often due to the restoring force characteristics of the base isolation system, i.e., variations in structural stiffness and damping during strong earth quakes. Stewart et al. (1999) identified several base-isolated 1Associate Professor,

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“…We denote p * = (p min + p max ) /2. This is always possible because although the hysteretic force may not be available on-line, an identification off-line is possible [26].…”

Section: Application: Control Of the Bouc-wen Hysteretic Oscillatormentioning

confidence: 99%

“…We consider that all the model parameters are defined within known intervals, without the need of knowing the exact values of the parameters. In practical problems, these intervals can be obtained through identification of real structures [26]. The effectiveness of the controller is shown by means of numerical simulations.…”

Section: Introductionmentioning

confidence: 99%

Adaptive backstepping control of some uncertain nonlinear oscillators

Ikhouane

Mañosa²,

Rodellar³

42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475)

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Abstract-A backstepping-based adaptive controller is designed for a class of uncertain second orded nonlinear systems under the strict-feedback form. It is shown that the closed loop is globally uniformly ultimately bounded and we give explicit bounds on both the asymptotic and transient performance. The control strategy is applied to a system typically found in base isolation schemes for seismic active protection of building structures. This system exhibits a hysteretic nonlinear behavior which is described analytically by the so-called Bouc-Wen model. Unlike other control schemes, the developed backstepping control does not require an exact knowledge of the model parameters. They are only defined within known intervals. The practical effectiveness of the controller is illustrated by numerical simulations.

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On-Line Parametric Identification of MDOF Nonlinear Hysteretic Systems

Cited by 177 publications

References 26 publications

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

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

System Identification of Base-Isolated Building using Seismic Response Data

Adaptive backstepping control of some uncertain nonlinear oscillators

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