TY - JOUR

T1 - A physics-informed Bayesian framework for characterizing ground motion process in the presence of missing data

AU - Chen, Yu

AU - Patelli, Edoardo

AU - Edwards, Benjamin

AU - Beer, Michael

N1 - Funding Information: This work was supported by the European Union Horizon 2020 Marie Skłodowska‐Curie Actions project URBASIS [Project no. 813137].

PY - 2023/5/9

Y1 - 2023/5/9

N2 - A Bayesian framework to characterize ground motions even in the presence of missing data is developed. This approach features the combination of seismological knowledge (a priori knowledge) 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.

AB - A Bayesian framework to characterize ground motions even in the presence of missing data is developed. This approach features the combination of seismological knowledge (a priori knowledge) 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.

KW - Bayesian model updating

KW - earthquake ground motion

KW - evolutionary power spectra

KW - missing data

KW - stochastic variational inference

KW - uncertainty quantification

UR - http://www.scopus.com/inward/record.url?scp=85150936833&partnerID=8YFLogxK

U2 - 10.1002/eqe.3877

DO - 10.1002/eqe.3877

M3 - Article

AN - SCOPUS:85150936833

VL - 52

SP - 2179

EP - 2195

JO - Earthquake Engineering and Structural Dynamics

JF - Earthquake Engineering and Structural Dynamics

SN - 0098-8847

IS - 7

ER -