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Model‐data fusion for seismic performance evaluation of an instrumented highway bridge
Author(s) -
Parida Siddharth S.,
Nikellis Alexandros,
Sett Kallol,
Singla Puneet
Publication year - 2020
Publication title -
earthquake engineering and structural dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.218
H-Index - 127
eISSN - 1096-9845
pISSN - 0098-8847
DOI - 10.1002/eqe.3317
Subject(s) - bridge (graph theory) , fuse (electrical) , probabilistic logic , earthquake engineering , nonlinear system , engineering , variance (accounting) , structural engineering , computer science , medicine , physics , electrical engineering , accounting , quantum mechanics , artificial intelligence , business
Summary 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.