Updating response sensitivity models of nonlinear vibrating structures using particle filters

Radhika, B.; Manohar, C.S.

doi:10.1016/j.compstruc.2011.02.008

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…are required to handle nonlinear parameter estimation problems. PF 34,35 is known as a robust approach in this endeavor. 22,36,37 Implementation of PF has concerns over its computational expense 38 and is therefore advisable as long as the parameter dimensions are manageable.…”

Section: Introductionmentioning

confidence: 99%

Strain‐based joint damage estimation approach robust to unknown non‐stationary input force

Aswal

Sen

Mevel

2022

Structural Contr & Hlth

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To avert catastrophic failure in the structures, joints are typically designed to yield, but not fail, so that energy accumulated under cyclic loading is dissipated. Eventually, this renders the structural joints to be characteristically weaker and more vulnerable than the members. Yet, damage detection research mostly assumes damage in the members only. This article proposes a model-based predictor-corrector algorithm that uses an interacting filtering approach to efficiently estimate joint damage in the presence of input and measurement uncertainties. For the predictor model, a novel straindisplacement relationship specific to semi-rigid frames is developed to map nodal displacements to corresponding strain measurements. The proposed estimation method embeds robustness against non-stationary input (e.g., seismic excitation) in the state filter, itself. For this, an output injection technique is integrated within the state filter. The modified state filter (robust Kalman filter) runs within an enveloping parameter filter (particle filter) to simultaneously estimate the system states and joint damage parameters, respectively, using the response signal. Strain has been adopted as measurement since it is frame independent (beneficial for seismic activity) and also comparatively cheaper to use. Numerical studies are performed on a two-dimensional (2-D) three-story three-bay shear frame for different joint damage locations and severities. The sensitivity and the stability of the proposed approach are further investigated. Experimental validation of the proposed algorithm is carried out on a 2-D steel frame.

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Section: Introductionmentioning

confidence: 99%

Strain‐based joint damage estimation approach robust to unknown non‐stationary input force

Aswal

Sen

Mevel

2022

Structural Contr & Hlth

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“…measurements), parameter estimation problems are inherently nonlinear. Over the years, Particle Filter (PF) [11][12][13] has been established as a better estimator than several other nonlinear variants of Kalman filter (KF) (e.g. Extended [8] or Unscented [14,15] KF) for nonlinear problems [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Seismic-induced damage detection through parallel force and parameter estimation using an improved interacting Particle-Kalman filter

Sen

Crinière

Mevel

et al. 2018

Mechanical Systems and Signal Processing

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Standard filtering techniques for structural parameter estimation assume that the input force is either known or can be replicated using a known white Gaussian model. Unfortunately for structures subjected to seismic excitation, the input time history is unknown and also no previously known representative model is available. This invalidates the aforementioned idealization. To identify seismic induced damage in such structures using filtering techniques, force must therefore also be estimated. In this paper, the input force is considered to be an additional state that is estimated in parallel to the structural parameters. Two concurrent filters are employed for parameters and force respectively. For the parameters, an interacting Particle-Kalman filter is used to target systems with correlated noise. Alongside this, a second filter is used to estimate the seismic force acting on the structure. In the proposed algorithm, the parameters and the inputs are estimated as being conditional on each other, thus ensuring stability in the estimation. The proposed algorithm is numerically validated on a sixteen degrees-of-freedom mass-spring-damper system and a five-story building structure.The stability of the proposed filter is also tested by subjecting it to a sufficiently long measurement time history. The estimation results confirm the applicability of the proposed algorithm.

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Nonlinear dynamic state estimation in instrumented structures with conditionally linear Gaussian substructures

Radhika

Manohar

2012

Probabilistic Engineering Mechanics

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Updating response sensitivity models of nonlinear vibrating structures using particle filters

Cited by 7 publications

References 20 publications

Strain‐based joint damage estimation approach robust to unknown non‐stationary input force

Strain‐based joint damage estimation approach robust to unknown non‐stationary input force

Seismic-induced damage detection through parallel force and parameter estimation using an improved interacting Particle-Kalman filter

Nonlinear dynamic state estimation in instrumented structures with conditionally linear Gaussian substructures

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