Reliability models for existing structures based on dynamic state estimation and data based asymptotic extreme value analysis

Radhika, B.; Manohar, C.S.

doi:10.1016/j.probengmech.2010.05.001

Cited by 21 publications

(9 citation statements)

References 36 publications

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“…By expressing the process and the measurement equations as in Eqs. (33), (34), the parameter identification problem is now transformed from the θ k -space to ξ k -space. However, the development of the equations for the dynamic state estimation does not get affected in any other way and the filtering is now carried out in the ξ k -space.…”

Section: Approachmentioning

confidence: 99%

“…The primary advantages of particle filters lie in their general nature and wide applicability for problems even with high degrees of nonlinearity. These methods have been used for system identification in a wide variety of problems such as, climate modeling [26], geophysics [27,28], heat transfer [29,30], diffusive transport [31], and structural health monitoring [32][33][34][35][36][37][38]. Particle filters require the solution of the forward problem for a large number of realizations for θ , generated using Monte Carlo simulations and evaluating their likelihood when compared with the measurement data.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2015

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“…This filtering procedure is applicable to nonlinear process and measurement equations, and, non‐Gaussian noise models. Some recent studies in which the scope of this method has been explored in structural engineering applications are by Manohar and Roy 14 and Radhika and Manohar 18.…”

Section: Bootstrap Filter For Dynamic State Estimationmentioning

confidence: 99%

“…A few studies that are available include the works of Ching et al . 13, Manohar and Roy 14, Namdeo and Manohar 15, Ghosh et al 16, Sajeeb et al 17, and Radhika and Manohar 18. In obtaining the estimates of the unknown structural system parameters, which are often time‐invariant in nature, one has three options: (a) declare the vector of system parameters as additional dynamic states thereby forming an extended state vector and implement dynamic state estimation algorithm on this extended set of process equations 7; (b) treat the unknown parameters as a set of discrete random variables and run a bank of filters with each constituent of this bank corresponding to one state of these discrete random variables 15; and (c) assume that the parameters are Gaussian random variables and employ a maximum likelihood estimation method for their determination 11.…”

Section: Introductionmentioning

confidence: 99%

Oxa‐azaspiro Derivatives: a Novel Class of Triple Re‐uptake Inhibitors

et al. 2010

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Drugs able to interfere with either the uptake or the metabolism of endogenous monoamines have been used for many years to treat depression. The first drugs of this type, such as the monoamine oxidase (MAO) inhibitors and the tricyclic antidepressants, achieved wide diffusion in the market, but are unfortunately associated with side effects that may influence patient compliance.[1] Over the last few years, compounds acting through the selective blockade of neurotransmitter re-uptake have demonstrated their efficacy as successful antidepressant agents. This blockade can take place in either serotoninergic neurons (SSRI; e.g., paroxetine) or noradrenergic neurons (SNRI; e.g., reboxetine). Moreover, drugs known as "dual uptake" inhibitors, which act on both serotoninergic and noradrenergic transporters (e.g., venlafaxine) or on both noradrenergic and dopaminergic transporters (e.g., bupropion), have also demonstrated good efficacy.[1]More recently, compounds acting as "triple re-uptake" inhibitors (TRUI), [2] such as indatraline and DOV 21,947, have been disclosed. Given the extensive experience of investigators at GlaxoSmithKline (GSK) with selective, dual and triple re-uptake inhibitors, new TRUI scaffolds were rationally planned exploiting a pharmacophore model developed in house More precisely, three specific pharmacophore models for the monoamine transporters, SERT (serotonin transporter), NET (norepinephrine transporter) and DAT (dopamine transporter), were built using structurally rigid and selective derivatives. These compounds were derived both from the GSK proprietary collection and from the public domain (e.g., derivatives described in Reference [3]). The structures were modeled within the Maestro modeling environment.[4] Conformational analyses were carried out for the three structures with BatchMin [5] using the following parameters: 1000 steps/rotatable bond, OPLS_ 2005 FF, implicit water model of solvation, 5000 minimization steps with PRCG. Conformations lying within a 3 kcal mol À1 energy window were kept.A single-ligand pharmacophore was then built over these structures using the Phase [6] module within Maestro, and selecting the most relevant pharmacophoric features. The three pharmacophores were finally merged together to create the triple re-uptake inhibitor pharmacophore. Average coordinates for the common pharmacophoric features were taken to determine their three-dimensional arrangement, as illustrated in Figure 1.The aim of the work described herein was twofold: the first objective was to set up the chemistry to prepare a new series of oxa-spiro derivatives; the second was to validate the capability of the pharmacophore model to predict the rank order of affinity of the new derivatives.It was hypothesized that the introduction of an appropriately located tetrahydrofuran (1) or a tetrahydropyran (2) moiety on the 4-phenyl piperidine scaffold should have satisfied at Figure 1. The triple re-uptake inhibitor pharmacophore. Coding of features: P6 sphere, positive ionizable; A2 sphere, H-bond ac...

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“…How to guarantee a high reliability level during the whole life-cycle for structures is a big challenge in practice, as it is obliged to rely upon efficient time-variant reliability analytical technologies [21,22]. To tackle the time dependency issue, two basic categories of methods in the current literature have been investigated including extreme performance-based approaches (the time-integrated/discretization methods) [23][24][25][26][27] and first-passage-based approaches (the analytical method with Rice's formula [28,29], the asymptotic method [30,31] or the system reliability-based approach [32,33]). The key point of the extreme performance approach is to obtain the extreme value of the performance under time-dependent input uncertainties and the use of the extreme value in combination with a critical threshold defined over the desired lifecycle estimate the actual reliability.…”

Section: Introductionmentioning

confidence: 99%

Time-variant reliability model and its measure index of structures based on a non-probabilistic interval process

Wang

Chen

et al. 2015

Acta Mech

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The study on reliability of an aging structure requires taking into account the influence of time. Typical approaches for performing time-variant reliability assessment are always based upon the random process model, where the dynamic distributions of uncertain parameters are determined by a substantial number of samples. In this paper, a new time-variant reliability measurement based on a non-probabilistic interval process model is proposed, in which we describe the time-varying uncertain variables at any time as intervals and define the corresponding auto-covariance function and correlation coefficient function to characterize the correlation between limit states at different times. By combining with the set-theory approach and the classical first-passage theory, a new non-probabilistic model of safety evaluation for time-dependent structures is established, and its measure index is then analytically calculated. The proposed model of timevariant reliability is suitable for both the cases of stationary process and non-stationary process. Moreover, the Monte Carlo method is also presented as a means of verification. The comparison between the presented model and the Monte Carlo-based model is eventually carried out on two application examples; the usage, efficiency and accuracy of the developed approach can be demonstrated.

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Reliability models for existing structures based on dynamic state estimation and data based asymptotic extreme value analysis

Cited by 21 publications

References 36 publications

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

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

Oxa‐azaspiro Derivatives: a Novel Class of Triple Re‐uptake Inhibitors

Time-variant reliability model and its measure index of structures based on a non-probabilistic interval process

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