Dynamic post-earthquake updating of regional damage estimates using Gaussian Processes

Lukas Bodenmann, Yves Reuland and Stojadinović, Božidar

Reliability Engineering and System Safety, 2023, vol. 234, issue C

Abstract: The widespread earthquake damage to the built environment induces severe short- and long-term societal consequences. Better community resilience may be achieved through well-organized recovery. Decisions to organize the recovery process are taken under intense time pressure using limited, and potentially inaccurate, data on the severity and the spatial distribution of building damage. We propose to use Gaussian Process inference models to fuse the available inspection data with a pre-existing earthquake risk model to dynamically update regional post-earthquake damage estimates and thereby support a well-organized recovery. The proposed method consistently aggregates the gradually incoming building damage inspection data to reduce the uncertainty in ground shaking intensity geographic distribution and to update regional building damage estimates. The performance of the proposed Gaussian Process methodology is demonstrated on one fictitious earthquake scenario and two real earthquake damage datasets. A comparison with purely data-driven methods shows that the proposed method reduces the number of building inspections required to provide reliable and precise damage predictions.

Keywords: Post-earthquake damage assessment; Gaussian Process models; Regional earthquake risk models; Uncertainty reduction (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023001163

DOI: 10.1016/j.ress.2023.109201

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