A Bootstrapped Modularised method of Global Sensitivity Analysis applied to Probabilistic Seismic Hazard Assessment

Francesco Di Maio, Nicola Gallo, Daniele Arcangeli, Matteo Taroni, Jacopo Selva and Enrico Zio ()
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Francesco Di Maio: POLIMI - Politecnico di Milano [Milan]
Nicola Gallo: POLIMI - Politecnico di Milano [Milan]
Daniele Arcangeli: UNIBO - Alma Mater Studiorum Università di Bologna = University of Bologna
Matteo Taroni: Istituto Nazionale di Geofisica e Vulcanologia
Jacopo Selva: Istituto Nazionale di Geofisica e Vulcanologia, University of Naples Federico II = Università degli studi di Napoli Federico II
Enrico Zio: POLIMI - Politecnico di Milano [Milan], CRC - Centre de recherche sur les Risques et les Crises - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres

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Abstract: Probabilistic Seismic Hazard Assessment (PSHA) evaluates the probability of exceedance of a given earthquake intensity threshold like the Peak Ground Acceleration, at a target site for a given exposure time. The stochasticity of the occurrence of seismic events is modelled by stochastic processes and the propagation of the earthquake wave in the soil is typically evaluated by empirical relationships called Ground Motion Prediction Equations. The large uncertainty affecting PSHA is quantified by defining alternative model settings and/or model parametrizations. In this work, we propose a novel Bootstrapped Modularised Global Sensitivity Analysis (BMGSA) method for identifying the model parameters most important for the uncertainty in PSHA, that consists in generating alternative artificial datasets by bootstrapping an available input-output dataset and aggregating the individual rankings obtained with the modularized method from each of those. The proposed method is tested on a realistic PSHA case study in Italy. The results are compared with a standard variance-based Global Sensitivity Analysis (GSA) method of literature. The novelty and strength of the proposed BMGSA method are both in the fact that its application only requires input-output data and not the use of a PSHA code for repeated calculations.

Keywords: Bootstrapped Modularised Global Sensitivity Analysis (BMGSA); Modularised Global Sensitivity Analysis (MGSA); Probabilistic Seismic Hazard Assessment (PSHA); Uncertainty; Acceleration; Earthquake effects; Equations of motion; Hazards; Motion estimation; Seismic response; Sensitivity analysis; Stochastic systems; Uncertainty analysis (search for similar items in EconPapers)
Date: 2023-03
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Published in Structural Safety, 2023, 101, pp.102312. ⟨10.1016/j.strusafe.2022.102312⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04103822

DOI: 10.1016/j.strusafe.2022.102312

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