TY - JOUR
T1 - Model-data fusion for seismic performance evaluation of an instrumented highway bridge
AU - Parida, Siddharth S.
AU - Nikellis, Alexandros
AU - Sett, Kallol
AU - Singla, Puneet
N1 - Publisher Copyright:
© 2020 John Wiley & Sons, Ltd.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - The paper presents a computationally efficient algorithm to integrate a probabilistic, non-Gaussian parameter estimation approach for nonlinear finite element models with the performance-based earthquake engineering (PBEE) framework for accurate performance evaluations of instrumented civil infrastructures. The algorithm first utilizes a minimum variance framework to fuse predictions from a numerical model of a civil infrastructure with its measured behavior during a past earthquake to update the parameters of the numerical model that is, then, used for performance prediction of the civil infrastructure during future earthquakes. A nonproduct quadrature rule, based on the conjugate unscented transformation, forms an enabling tool to drive the computationally efficient model prediction, model-data fusion, and performance evaluation. The algorithm is illustrated and validated on Meloland Road overpass, a heavily instrumented highway bridge in El Centro, CA, which experienced three moderate earthquake events in the past. The benefits of integrating measurement data into the PBEE framework are highlighted by comparing damage fragilities of and annual probabilities of damages to the bridge estimated using the presented algorithm with that estimated using the conventional PBEE approach.
AB - The paper presents a computationally efficient algorithm to integrate a probabilistic, non-Gaussian parameter estimation approach for nonlinear finite element models with the performance-based earthquake engineering (PBEE) framework for accurate performance evaluations of instrumented civil infrastructures. The algorithm first utilizes a minimum variance framework to fuse predictions from a numerical model of a civil infrastructure with its measured behavior during a past earthquake to update the parameters of the numerical model that is, then, used for performance prediction of the civil infrastructure during future earthquakes. A nonproduct quadrature rule, based on the conjugate unscented transformation, forms an enabling tool to drive the computationally efficient model prediction, model-data fusion, and performance evaluation. The algorithm is illustrated and validated on Meloland Road overpass, a heavily instrumented highway bridge in El Centro, CA, which experienced three moderate earthquake events in the past. The benefits of integrating measurement data into the PBEE framework are highlighted by comparing damage fragilities of and annual probabilities of damages to the bridge estimated using the presented algorithm with that estimated using the conventional PBEE approach.
UR - http://www.scopus.com/inward/record.url?scp=85088365132&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85088365132&partnerID=8YFLogxK
U2 - 10.1002/eqe.3317
DO - 10.1002/eqe.3317
M3 - Article
AN - SCOPUS:85088365132
VL - 49
SP - 1559
EP - 1578
JO - Earthquake Engineering and Structural Dynamics
JF - Earthquake Engineering and Structural Dynamics
SN - 0098-8847
IS - 14
ER -