A Bayesian network-based probabilistic framework for updating aftershock risk of bridges

Enrico Tubaldi, Francesca Turchetti, Ekin Ozer, Jawad Fayaz, Pierre Gehl, Carmine Galasso

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
51 Downloads (Pure)

Abstract

The evaluation of a bridge's structural damage state following a seismic event and the decision on whether or not to open it to traffic under the threat of aftershocks (ASs) can significantly benefit from information about the mainshock (MS) earthquake's intensity at the site, the bridge's structural response, and the resulting damage experienced by critical structural components. This paper illustrates a Bayesian network (BN)-based probabilistic framework for updating the AS risk of bridges, allowing integration of such information to reduce the uncertainty in evaluating the risk of bridge failure. Specifically, a BN is developed for describing the probabilistic relationship among various random variables (e.g., earthquake-induced ground-motion intensity, bridge response parameters, seismic damage, etc.) involved in the seismic damage assessment. This configuration allows users to leverage data observations from seismic stations, structural health monitoring (SHM) sensors and visual inspections (VIs). The framework is applied to a hypothetical bridge in Central Italy exposed to earthquake sequences. The uncertainty reduction in the estimate of the AS damage risk is evaluated by utilising various sources of information. It is shown that the information from accelerometers and VIs can significantly impact bridge damage estimates, thus affecting decision-making under the threat of future ASs.

Original languageEnglish
Pages (from-to)2496-2519
Number of pages24
JournalEarthquake Engineering and Structural Dynamics
Volume51
Issue number10
Early online date26 Jun 2022
DOIs
Publication statusPublished - 1 Aug 2022

Bibliographical note

Funding Information:
This paper is supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No 821046, project TURNkey (Towards more Earthquake‐resilient Urban Societies through a Multi‐sensor‐based Information System enabling Earthquake Forecasting, Early Warning and Rapid Response actions). Input and feedback from Gemma Cremen and Dr Elisa Zuccolo on the draft manuscript is greatly appreciated.

Publisher Copyright:
© 2022 The Authors. Earthquake Engineering & Structural Dynamics published by John Wiley & Sons Ltd.

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