Kriging Metamodeling-Based Monte Carlo Simulation for Improved Seismic Fragility Analysis of Structures

Ghosh, Shyamal; Roy, Atin; Chakraborty, Subrata

doi:10.1080/13632469.2019.1570395

Cited by 22 publications

(9 citation statements)

References 40 publications

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“…Fragility analysis and, more broadly, seismic risk assessment based on stochastic simulations is not new, and an incomplete list of studies includes [11,12,13,14]. Moreover, to the best of our knowledge, the first study using also kriging surrogate modeling is [15] and more recently [16]. Different form the previous contributions, this paper draws a direct link between ground motion selection and the calibration of the stochastic hazard model; moreover, it introduces HK to combine predictions of simulators with different degrees of fidelity.…”

Section: R a F Tmentioning

confidence: 99%

Seismic fragility analysis based on artificial ground motions and surrogate modeling of validated structural simulators

Abbiati

Broccardo

Abdallah

et al. 2021

Earthq Engng Struct Dyn

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This study introduces a computational framework for efficient and accurate seismic fragility analysis based on a combination of artificial ground motion modeling, polynomial-chaos-based global sensitivity analysis, and hierarchical kriging surrogate modeling. The framework follows the philosophy of the Performance-Based Earthquake Engineering PEER approach, where the fragility analysis is decoupled from hazard analysis. This study addresses three criticalities that are present in the current practice. Namely, reduced size of hazardconsistent size-specific ensembles of seismic records, validation of structural simulators against large-scale experiments, high computational cost for accurate fragility estimates. The effectiveness of the proposed framework is demonstrated for the Rio Torto Bridge, recently tested using hybrid simulation within the RETRO project.

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Section: R a F Tmentioning

confidence: 99%

Seismic fragility analysis based on artificial ground motions and surrogate modeling of validated structural simulators

Abbiati

Broccardo

Abdallah

et al. 2021

Earthq Engng Struct Dyn

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“…They can be built from a few numbers of response samples generated through accurate numerical models or experimental data (Bhosekar and Ierapetritou 2018). Several LF approaches are available in the literature, which are based either on regression or interpolation models; in particular, high-dimensional model representations, polynomial regressions, artificial neural networks, Bayesian networks, multivariate adaptive regression splines, radial basis function networks, support vector regressions, and Kriging were proposed (Van Beers and Kleijnen 2003;Kleijnen 2017;Forrester et al 2008).…”

Section: Background and Motivationmentioning

confidence: 99%

“…Inevitable drawbacks are related to: (1) the inversion of the covariance matrix, whose size could entail serious computational problems; (2) the estimation of the hyperparameters that involves a constrained iterative search. This why kriging was successfully used both in seismic risk assessment and seismic fragility analysis (Gidaris et al 2015;Ghosh et al 2019), and in real-time storm/hurricane risk assessment (Jia and Taflanidis 2013). As a result, Gidaris et al (2015) adopted a Kriging model to approximate mean and standard deviation values of structural demands, allowing to analytically evaluate seismic fragility functions of a four-story concrete office building.…”

Section: Background and Motivationmentioning

confidence: 99%

Seismic vulnerability of above-ground storage tanks with unanchored support conditions for Na-tech risks based on Gaussian process regression

Phan

Paolacci

Filippo

et al. 2020

Bull Earthquake Eng

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This paper aims to investigate the seismic vulnerability of an existing unanchored steel storage tank ideally installed in a refinery in Sicily (Italy), along the lines of performance-based earthquake engineering. Tank performance is estimated by means of component-level fragility curves for specific limit states. The assessment is based on a framework that relies on a three-dimensional finite element (3D FE) model and a low-fidelity demand model based on Gaussian process regression, which allows for cheaper simulations. Moreover, to approximate the system response corresponding to the random variation of both peak ground acceleration and liquid filling level, a second-order design of experiments method is adopted. Hence, a parametric investigation is conducted on a specific existing unanchored steel storage tank. The relevant 3D FE model is validated with an experimental campaign carried out on a shaking table test. Special attention is paid to the base uplift due to significant inelastic deformations that occur at the baseplate close to the welded baseplate-to-wall connection, offering extensive information on both capacity and demand. As a result, the tank performance is estimated by means of component-level fragility curves for the aforementioned limit state which are derived through Monte Carlo simulations. The flexibility of the proposed framework allows fragility curves to be derived considering both deterministic and random filling levels. The comparison of the seismic vulnerability of the tank obtained with probabilistic and deterministic mechanical models demonstrates the conservatism of the latter. The same trend is also exhibited in terms of risk assessment.

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“…the ground motion parameters, iv) dimensionality reduction of the parameter space of the AGM model, v) computation of a hierarchical kriging (HK) surrogate that fuses LF and HF realizations of the QoI, vi) fragility analysis computed via Monte-Carlo-based UQ forward analysis of the HK surrogate. Fragility analysis and, more broadly, seismic risk assessment based on stochastic simulations and Kriging surrogate modeling was firstly proposed in [33] and, more recently, adopted in [34]. Unlike [33,34], Abbiati et al [29] introduced global sensitivity analysis based on PCE and HK to reduce further the computational cost of the fragility analysis.…”

Section: R a F Tmentioning

confidence: 99%

“…Fragility analysis and, more broadly, seismic risk assessment based on stochastic simulations and Kriging surrogate modeling was firstly proposed in [33] and, more recently, adopted in [34]. Unlike [33,34], Abbiati et al [29] introduced global sensitivity analysis based on PCE and HK to reduce further the computational cost of the fragility analysis. The resulting framework integrates state-of-the-art know-how in ground motion selections and follows the original spirit of the PEER-PBEE framework, which decouples hazard and fragility analysis.…”

Section: R a F Tmentioning

confidence: 99%

Seismic Fragility Analysis of a Coupled Tank-Piping System based on Artificial Ground Motions and Surrogate Modeling

Abbiati¹,

Broccardo²,

Filippo³

et al. 2020

Preprint

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The catastrophic consequences of recent NaTech events triggered by earthquakes highlighted the inadequacy of standard approaches to seismic risk assessment of chemical process plants. To date, the risk assessment of such facilities mainly relies on historical data and focuses on uncoupled process components. As a consequence, the dynamic interaction between process equipment is neglected. In response to this gap, researchers started a progressive integration of the Pacific Earthquake Engineering Research Center (PEER) Performance-Based Earthquake Engineering (PBEE) risk assessment framework. However, a few limitations still prevent a systematic implementation of this framework to chemical process plants. The most significant are: i) the computational cost of system-level simulations accounting for coupling between process equipment; ii) the experimental cost for component-level model validation; iii) a reduced number of hazard-consistent site-specific ground motion records for time history analyses.In response to these challenges, this paper proposes a recently developed uncertainty quantification-based framework to perform seismic fragility assessments of chemical process plants. The framework employs three key elements: i) a stochastic ground-motion model to supplement scarcity of real records; ii) surrogate modeling to reduce the computational cost of system-level simulations; iii) a component-level model validation based on cost-effective hybrid simulation tests. In order to demonstrate the potential of the framework, two fragility functions are computed for a pipe elbow of a coupled tank-piping system.

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Kriging Metamodeling-Based Monte Carlo Simulation for Improved Seismic Fragility Analysis of Structures

Abstract: The material cannot be used for any other purpose without further permission of the publisher and is for private use only.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.

Cited by 22 publications

References 40 publications

Seismic fragility analysis based on artificial ground motions and surrogate modeling of validated structural simulators

Seismic fragility analysis based on artificial ground motions and surrogate modeling of validated structural simulators

Seismic vulnerability of above-ground storage tanks with unanchored support conditions for Na-tech risks based on Gaussian process regression

Seismic Fragility Analysis of a Coupled Tank-Piping System based on Artificial Ground Motions and Surrogate Modeling

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