Obtaining refined first‐order predictive models of linear structural systems

Lu, Hilmi; Betti, Raimondo; Longman, Richard W.

doi:10.1002/eqe.169

Cited by 38 publications

(2 citation statements)

References 24 publications

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“…Modifications of the Kalman filter that may allow for the simultaneous 45 identification of the input force ( [10]) and methods based on observers of similar nature ( [11,12]) are also liable to such effects. In the case of non-smooth sys-tems in particular, the fact that a parameter may be unidentifiable over some time interval, may also result in the divergence of the predicted values when employing these methods during this interval.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, one needs to select the elements ofx and P that correspond to the observable components, which will be updated according to equations (9) and (10). The unobservable states and corresponding covariance terms are 285 held constant, while the P uo terms are updated according to equations (11) and (12). Table 3 summarizes the method used for the unobservable and observable parts ofx and P. A schematic representation of the DEKF is presented in Figure 1.…”

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confidence: 99%

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A Discontinuous Extended Kalman Filter for non-smooth dynamic problems

Chatzis

Chatzi

Triantafyllou

2017

Mechanical Systems and Signal Processing

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Problems that result into locally non-differentiable and hence non-smooth state-space equations are often encountered in engineering. Examples include problems involving material laws pertaining to plasticity, impact and highly non-linear phenomena. Estimating the parameters of such systems poses a challenge, particularly since the majority of system identification algorithms 5 are formulated on the basis of smooth systems under the assumption of observability, identifiability and time invariance. For a smooth system, an observable state remains observable throughout the system evolution with the exception of few selected realizations of the state vector. However, for a non-smooth system the observable set of states and parameters may vary during the evolution of the 10 system throughout a dynamic analysis. This may cause standard identification (ID) methods, such as the Extended Kalman Filter, to temporarily diverge and ultimately fail in accurately identifying the parameters of the system. In this work, the influence of observability of non-smooth systems to the performance of the Extended and Unscented Kalman Filters is discussed and a novel algo-15 rithm particularly suited for this purpose, termed the Discontinuous Extended Kalman Filter (DEKF), is proposed.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

A Discontinuous Extended Kalman Filter for non-smooth dynamic problems

Chatzis

Chatzi

Triantafyllou

2017

Mechanical Systems and Signal Processing

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System identification of a building from multiple seismic records

Ulusoy

Feng

Fanning

2010

Earthq Engng Struct Dyn

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This paper describes the identification of finite dimensional, linear, time-invariant models of a 4-story building in the state space representation using multiple data sets of earthquake response. The building, instrumented with 31 accelerometers, is located on the University of California, Irvine campus. Multiple data sets, recorded during the 2005 Yucaipa, 2005 San Clemente, 2008 Chino Hills and 2009 Inglewood earthquakes, are used for identification and validation. Considering the response of the building as the output and the ground motion as the input, the state space models that represent the underlying dynamics of the building in the discrete-time domain corresponding to each data set are identified. The time-domain Eigensystem Realization Algorithm with the Observer/Kalman filter identification procedure are adopted in this paper, and the modal parameters of the identified models are consistently determined by constructing stabilization diagrams. The four state space models identified demonstrate that the response of the building is amplitude dependent with the response frequency and damping, being dependent on the magnitude of ground excitation. The practical application of this finding is that the consistency of this building response to future earthquakes can be quickly assessed, within the range of ground excitations considered (0.005g-0.074g), for consistency with prior response-this assessment of consistent response is discussed and demonstrated with reference to the four earthquake events considered in this study. Inclusion of data sets relating to future earthquakes will enable the findings to be extended to a wider range of ground excitation magnitudes. the system. In what follows, the identification problem reduces to the estimation of unknown parameters guided by observation data [1].Identification techniques require excitation (input) and response (output) measurements for a complete determination of a model. In order to obtain input and output data, one needs to perform an experiment/test on the system/structure under study. For instance, in modal testing, it is a common practice to excite the test structure by applying measurable excitations at several points, then collect response data at the sensor locations [2]. However, many civil engineering structures are difficult to excite artificially due to their large size, geometry and location. Equally a large amount of external energy is needed to excite an entire structure at a desired level of vibration. Besides, even if artificial excitation is provided, civil engineering structures, which by necessity are tested in situ, continue to be excited by other unmeasurable forces (wind, waves and traffic for example) and it is thus not possible to confidently declare that any measured response is necessarily only due to any artificial excitation provided. In order to deal in part with such difficulties, some identification algorithms have been developed based on output only vibration data due to ambient and environmental forces, i.e. traffic, wind...

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Rapid evaluation and damage assessment of instrumented highway bridges

Mosquera

Smyth

Betti

2011

Earthq Engng Struct Dyn

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SUMMARYThis study focuses on the use of strong motion data recorded during earthquakes and aftershocks to provide a preliminary assessment of the structural integrity and possible damage in bridges. A system identification technique is used to determine dynamical characteristics and high-fidelity first-order linear models of a bridge from low level earthquake excitations. A finite element model is developed and updated using a genetic algorithm optimization scheme to match the frequencies identified and to simulate data from a damaging earthquake for the bridge. Here, two criteria are used to determine the state of the structure. The first criteria uses the error between the data recorded or simulated by the calibrated nonlinear finite element model and the data predicted by the linear model. The second criteria compares relative displacements of the structure with displacement thresholds identified using a pushover analysis. The use of this technique can provide an almost immediate, yet reliable, assessment of the structural health of an instrumented bridge after a seismic event.

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Obtaining refined first‐order predictive models of linear structural systems

Cited by 38 publications

References 24 publications

A Discontinuous Extended Kalman Filter for non-smooth dynamic problems

A Discontinuous Extended Kalman Filter for non-smooth dynamic problems

System identification of a building from multiple seismic records

Rapid evaluation and damage assessment of instrumented highway bridges

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