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Efficient loss analysis through surrogate probabilistic seismic demand models

Gentile, R; Galasso, C; (2022) Efficient loss analysis through surrogate probabilistic seismic demand models. In: Proceedings of the The 13th International Conference on Structural Safety and Reliability (ICOSSAR 2021). (pp. pp. 1-10). ICOSSAR Green open access

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Abstract

This paper discusses a recently-proposed statistical model mapping the parameters controlling the dynamic behaviour of inelastic single-degree-of-freedom (SDoF) systems (i.e. force-displacement capacity curve, hysteretic behaviour) and the parameters of their probabilistic seismic demand model (PSDM, i.e. conditional distribution of an engineering demand parameter given a ground-motion intensity measure). This metamodel allows rapidly deriving fragility curves of equivalent SDoF systems. The model involves Gaussian Process (GP) regressions trained using 10,000 SDoF systems analysed via cloud-based non-linear timehistory analysis (NLTHA) using natural ground motions. A 10-fold cross validation is used to test the GP regressions, predicting the PSDM of both the SDoF database and eight realistic reinforced concrete (RC) frames, benchmarking the results against NLTHA. Error levels are deemed satisfactory for practical applications, especially considering the low required modelling effort and analysis time. Two possible applications of the proposed metamodel are briefly discussed: direct loss-based seismic design and portfolio risk modelling with dynamic representations of exposure and vulnerability modules.

Type: Proceedings paper
Title: Efficient loss analysis through surrogate probabilistic seismic demand models
Event: 13th International Conference on Structural Safety & Reliability (ICOSSAR 2021-22)
Location: Shanghai, China
Open access status: An open access version is available from UCL Discovery
Publisher version: http://www.icossar2021.org/Data/List/Welcome
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Gaussian process regression; metamodeling; direct loss-based seismic design; dynamic earthquake risk modelling
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Inst for Risk and Disaster Reduction
URI: https://discovery.ucl.ac.uk/id/eprint/10180480
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