A physics-informed Bayesian framework for characterizing ground motion process in the presence of missing data

Chen, Yu ORCID: 0000-0001-6617-2946, Patelli, Edoardo ORCID: 0000-0002-5007-7247, Edwards, Benjamin ORCID: 0000-0001-5648-8015 and Beer, Michael ORCID: 0000-0002-0611-0345 (2023) A physics-informed Bayesian framework for characterizing ground motion process in the presence of missing data. EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS, 52 (7). pp. 2179-2195.

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Official URL: http://dx.doi.org/10.1002/eqe.3877

Abstract

<jats:title>Abstract</jats:title><jats:p>A Bayesian framework to characterize ground motions even in the presence of missing data is developed. This approach features the combination of seismological knowledge (<jats:italic>a priori knowledge</jats:italic>) with empirical observations (even incomplete) via Bayesian inference. At its core is a Bayesian neural network model that probabilistically learns temporal patterns from ground motion data. Uncertainties are accounted for throughout the framework. Performance of the approach has been quantitatively demonstrated via various missing data scenarios. This framework provides a general solution to dealing with missing data in ground motion records by providing various forms of representation of ground motions in a probabilistic manner, allowing it to be adopted for numerous engineering and seismological applications. Notably, it is compatible with the versatile Monte Carlo simulation scheme, such that stochastic dynamic analyses are still achievable even with missing data. Furthermore, it serves as a complementary approach to current stochastic ground‐motion models in data‐scarce regions under the growing interests of PBEE (performance‐based earthquake engineering), mitigating the data‐model dependence dilemma due to the paucity of data, and ultimately, as a fundamental solution to the limited data problem in data scarce regions.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	Bayesian model updating, earthquake ground motion, evolutionary power spectra, missing data, stochastic variational inference, uncertainty quantification
Divisions:	Faculty of Science and Engineering > School of Engineering Faculty of Science and Engineering > School of Environmental Sciences
Depositing User:	Symplectic Admin
Date Deposited:	24 Apr 2023 07:30
Last Modified:	04 Sep 2023 02:47
DOI:	10.1002/eqe.3877
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3169893