UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Gaussian process regression for seismic fragility assessment of building portfolios

Gentile, R; Galasso, C; (2020) Gaussian process regression for seismic fragility assessment of building portfolios. Structural Safety , 87 , Article 101980. 10.1016/j.strusafe.2020.101980. Green open access

[thumbnail of Gentile_gp structural safety R2.pdf]
Preview
Text
Gentile_gp structural safety R2.pdf - Accepted Version

Download (3MB) | Preview

Abstract

Seismic fragility assessment of building portfolios is often based on the analysis of “average” building models representative of structural types (or building classes), thus neglecting building-to-building variability within a structural type. This paper proposes the use of Gaussian process (GP) regressions to develop flexible and accurate metamodels explicitly mapping building-class attributes to the seismic fragility parameters. The proposed metamodels can enable analysts to account for building-to-building variability in simulation-based seismic risk assessment of building portfolios. Unlike other commonly-used metamodels, GP regressions do not require the a-priori definition of a prediction function and they quantify the uncertainty on the predictions in a refined and explicit fashion. The proposed method is demonstrated for a portfolio of seismically-deficient reinforced concrete school buildings with construction details typical of some developing countries. Based on the available information about the building attributes (e.g. geometry, materials, detailing), building realisations are generated based on two alternative approaches, which are critically compared: design of experiment and Monte Carlo sampling. Cloud-based time-history analysis for each building realisation is performed using unscaled real ground-motion records; fragility relationships are derived for four structure-specific damage states. A GP regression is then developed for each considered fragility parameter (i.e. median and dispersion). To further increase the tractability and scalability of the methodology, alternative metamodels are defined based on numerical non-linear static pushover analyses or analytical “by-hand” pushover analyses, through the Simple Lateral Mechanism Analysis (SLaMA) method. The results show that, for the considered portfolio, the fitted GP regressions have a high predictive power in surrogating the modelled fragility, demonstrating the feasibility of the approach in practice. It is also shown that the choice of the sampling technique could be based on the input data availability, rather than on the expected computational burden. Finally, the use of simplified methods for response analysis shows acceptable error levels with respect to the full time-history analysis results. Such simplified methods can be promising alternatives to generate large training datasets for the proposed GP regressions. This increases the potential of training metamodels in practical portfolio risk assessment applications, in which a high number of building types, each characterised by a large number of attributes, is generally involved.

Type: Article
Title: Gaussian process regression for seismic fragility assessment of building portfolios
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.strusafe.2020.101980
Publisher version: https://doi.org/10.1016/j.strusafe.2020.101980
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Gaussian process regression, Seismic fragility, Mechanics-based approaches, Metamodels, Building-to-building variability, Building portfolios
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
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/10110306
Downloads since deposit
196Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item