Robust Structural Health Monitoring Using a Polynomial Chaos Based Sequential Data Assimilation Technique

Saad, George; Ghanem, Roger

doi:10.1007/978-94-007-5134-7_12

Cited by 5 publications

(6 citation statements)

References 9 publications

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“…The numerical problem in this paper is similar to the one found in [16,24,25] with some slight modifications. It consists of a four-degree-of-freedom shear building, as shown in figure 1 below, and subjected to El-Centro earthquake excitation at its base.…”

Section: Numerical Examplementioning

confidence: 87%

Sensor Placement Optimization Using Ensemble Kalman Filter and Genetic Algorithm

Nasr¹,

Saad²

2015

Proceedings of the 5th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Section: Numerical Examplementioning

confidence: 87%

Sensor Placement Optimization Using Ensemble Kalman Filter and Genetic Algorithm

Nasr¹,

Saad²

2015

Proceedings of the 5th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…The use of PCE within Bayesian frameworks for parameter estimation in inverse problems is of recent vintage; see for example, [31,[49][50][51][52][53][54][55][56]. These studies use PCE to analyse the propagation of uncertainty through a system where the unknown parameters have been modelled as random variables/random fields.…”

Section: Discussionmentioning

confidence: 99%

“…Subsequently, estimates of the unknowns have been obtained from the posterior probability density functions which, in most studies, have been approximated by minimising the covariance. Thus, these methods are based on the principles of Kalman filter and its variants [50][51][52][53][54][55][56] and involve linearizations or other forms of local approximations. In contrast, this paper proposes a particle filter based methodology and is more generally applicable irrespective of the nonlinearity in the problem.…”

Section: Discussionmentioning

confidence: 99%

The use of polynomial chaos for parameter identification from measurements in nonlinear dynamical systems

2015

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This study focuses on the development of a computationally efficient algorithm for the offline identification of system parameters in nonlinear dynamical systems from noisy response measurements. The proposed methodology is built on the bootstrap particle filter available in the literature for dynamic state estimation. The model and the measurement equations are formulated in terms of the system parameters to be identified ‐ treated as random variables, with all other parameters being considered as internal variables. Subsequently, the problem is transformed into a mathematical subspace spanned by a set of orthogonal basis functions obtained from polynomial chaos expansions of the unknown system parameters. The bootstrap filtering carried out in the transformed space enables identification of system parameters in a computationally efficient manner. The efficiency of the proposed algorithm is demonstrated through two numerical examples ‐ a Duffing oscillator and a fluid structure interaction problem involving an oscillating airfoil in an unsteady flow.

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“…To address this issue, this study proposes the use of polynomial chaos based Kalman filter for data assimilation purposes. PCKF was suggested as a sampling free alternative [8,9,10,11]. A detailed explanation of its theoretical background and implementation is discussed below.…”

Section: Sequential Data Assimilationmentioning

confidence: 99%

A Robust Polynomial Chaos Kalman Filter Framework for Corrosion Detection in Reinforced Concrete Structures

Slika¹,

Saad²

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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A main reason behind reinforced concrete structural deterioration is chlorideinduced corrosion. Once the critical chloride concentration is exceeded at the rebar level, the structure becomes susceptible to corrosion initiation. Corrosion propagates progressively, degrades the resistance capacity of the structure and decreases the design safety margin. To mitigate this risk, a stochastic sequential data assimilation technique based on chloride concentration measurements and the Polynomial Chaos Kalman Filter (PCKF) is presented. The Power of PCKF lies in its sampling free scheme and polynomial structure to represent uncertainty. In modeling chloride ingress mechanism, different independent sources of uncertainty should be incorporated in the system including, mathematical model simplification errors, parametric errors, boundary condition errors, and time independent sensors errors. In such circumstances, the curse of dimensionality hinders the efficiency and the applicability of PCKF, due to the exponential growth of the required bases to account for the added uncertainties. This study presents a practical framework to maintain an acceptable accuracy of PCKF without scarifying the computational efficiency of the filter. A one dimensional synthetic numerical example is presented to verify the efficiency of the proposed implementation scheme.

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Robust Structural Health Monitoring Using a Polynomial Chaos Based Sequential Data Assimilation Technique

Cited by 5 publications

References 9 publications

Sensor Placement Optimization Using Ensemble Kalman Filter and Genetic Algorithm

Sensor Placement Optimization Using Ensemble Kalman Filter and Genetic Algorithm

The use of polynomial chaos for parameter identification from measurements in nonlinear dynamical systems

A Robust Polynomial Chaos Kalman Filter Framework for Corrosion Detection in Reinforced Concrete Structures

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