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

Abbiati, Giuseppe; Broccardo, Marco; Abdallah, Imad; Marelli, Stefano; Paolacci, Fabrizio

doi:10.1002/eqe.3448

Cited by 21 publications

(26 citation statements)

References 59 publications

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“…Recently, Abbiati et al [29] proposed a comprehensive framework that tackles these open challenges. This study adopts and applies this framework to compute the seismic fragility analysis of a tank-piping system of an ideal CPP.…”

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%

“…The theoretical framework is based on the work described in [29]. In order to present a self-contained study, the main parts of the methodology are described below.…”

Section: Computational Framework For Fragility Analysismentioning

confidence: 99%

“…For further details, the reader should consult the stated D R A F T reference. Following [29], a seismic ground motion is represented by a parametric stochastic process, A(t). Two sources of uncertainties are here considered.…”

Section: Computational Framework For Fragility Analysismentioning

confidence: 99%

“…To define an optimal surrogate model w.r.t. the stochastic input, the computational solver, and QoI [29] designed a strategy that builds on PCE-based GSA and HK. Here, we summarize the main steps.…”

Section: Computational Framework For Fragility Analysismentioning

confidence: 99%

See 4 more Smart Citations

Seismic fragility analysis of a coupled tank-piping system based on artificial ground motions and surrogate modeling

Abbiati¹,

Broccardo

Filippo

et al. 2021

Journal of Loss Prevention in the Process Industries

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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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Section: R a F Tmentioning

confidence: 99%

Section: R a F Tmentioning

confidence: 99%

“…The theoretical framework is based on the work described in [29]. In order to present a self-contained study, the main parts of the methodology are described below.…”

Section: Computational Framework For Fragility Analysismentioning

confidence: 99%

Section: Computational Framework For Fragility Analysismentioning

confidence: 99%

Section: Computational Framework For Fragility Analysismentioning

confidence: 99%

See 3 more Smart Citations

Seismic fragility analysis of a coupled tank-piping system based on artificial ground motions and surrogate modeling

Abbiati¹,

Broccardo

Filippo

et al. 2021

Journal of Loss Prevention in the Process Industries

Self Cite

View full text Add to dashboard Cite

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Probabilistic seismic performance estimation through surrogate model and unbiased multi‐fidelity Monte Carlo predictor

Gao,

Iyama,

Itoi

2024

Japan Architectural Review

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This study introduces an approach for probabilistic seismic performance estimation, which focuses on the probability of intensity measures exceeding a specified value based on engineering demand parameters. Conventional methods face challenges owing to the increase in computational costs associated with the uncertainties in earthquake scenarios. To address this, we use high‐fidelity (HF) and low‐fidelity (LF) model data to develop a multilevel hierarchy of surrogate models, which improves the simulation‐based probabilistic estimation. However, designing a reliable LF model and ensuring the accuracy of the surrogate model hierarchy remains challenging. Herein, we present a multi‐fidelity Monte Carlo (MFMC) predictor combined with a conventional surrogate model to improve probabilistic seismic performance estimation, thereby leveraging LF model efficiency and HF data accuracy for unbiased results. We addressed the challenge of constructing a suitable LF model by using a surrogate model trained from limited HF data. The MFMC predictor improves the accuracy of probabilistic analysis than the surrogate models trained from limited HF data. Further, the automatic relevance determination method is introduced to select the most appropriate inputs for the surrogate model. A case study featuring a special moment‐resistant frame, subject to uncertainties from both ground motion and structural properties, illustrates the efficiency of the method. A LF model is developed using the Kriging method, followed by the construction of the MFMC predictor for probabilistic analysis, thereby leveraging both limited HF and numerous LF data. Comparing the exceedance probability curves obtained from the MFMC predictor with those from direct Monte Carlo simulations and conventional Kriging showed that our proposed method offers a promising tool for probabilistic seismic performance estimation under uncertainty.

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A multi‐fidelity Bayesian framework for robust seismic fragility analysis

Sevieri

Gentile

Galasso

2021

Earthq Engng Struct Dyn

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Fragility analysis of structures via numerical methods involves a complex trade‐off between the desired accuracy, the explicit consideration of uncertainties (both epistemic and aleatory) related to the numerical structural model and the available computational performance. This paper introduces a framework for deriving numerical fragility relationships based on multi‐fidelity non‐linear models of the structure under investigation and response‐analysis types. The proposed framework aims to reduce the computational burden while achieving a desired accuracy of the fragility estimates without neglecting aleatory and epistemic uncertainties. The proposed approach is an extension of the well‐known robust fragility (RF) analysis framework. Different model classes, each characterised by increasing refinement, are used to define multi‐fidelity polynomial expansions of the fragility model parameters. Each analysis result is then considered as a ‘new observation’ in a Bayesian framework and used to update the coefficients of the polynomial expansions. An adaptive sampling algorithm is also proposed to futher improve the performance of the multi‐fidelity framework. Specifically, such an adaptive sampling algorithm relies on partitioning the sample space and the Kullback–Leibler divergence to find the optimal sampling path. The sample space partitioning allows an analyst to specify different criteria and parameters of the algorithm for different regions, thus further improving the performance of the procedure. The proposed approach is illustrated for an archetype reinforced concrete (RC) frame for which two model classes are developed/analysed: the simple lateral mechanism analysis (SLaMA), coupled with the capacity spectrum method, and non‐linear dynamic analysis. Both model classes involve a cloud‐based approach employing unscaled real (i.e. recorded) ground motions. The fragility relationships derived with the proposed procedure are finally compared to those calculated by using only the most advanced/high‐fidelity (HF) model class, thus quantifying the performance of the proposed approach and highlighting further research needs.

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

Cited by 21 publications

References 59 publications

Seismic fragility analysis of a coupled tank-piping system based on artificial ground motions and surrogate modeling

Seismic fragility analysis of a coupled tank-piping system based on artificial ground motions and surrogate modeling

Probabilistic seismic performance estimation through surrogate model and unbiased multi‐fidelity Monte Carlo predictor

A multi‐fidelity Bayesian framework for robust seismic fragility analysis

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