Stochastic filtering for damage identification through nonlinear structural finite element model updating

Rodrigo Astroza; Hamed Ebrahimian; Joel P. Conte

doi:10.1117/12.2084522

27 March 2015 Stochastic filtering for damage identification through nonlinear structural finite element model updating

Rodrigo Astroza, Hamed Ebrahimian, Joel P. Conte

Proceedings Volume 9435, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015; 94350T (2015) https://doi.org/10.1117/12.2084522
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2015, San Diego, California, United States

Abstract

This paper describes a novel framework that combines advanced mechanics-based nonlinear (hysteretic) finite element (FE) models and stochastic filtering techniques to estimate unknown time-invariant parameters of nonlinear inelastic material models used in the FE model. Using input-output data recorded during earthquake events, the proposed framework updates the nonlinear FE model of the structure. The updated FE model can be directly used for damage identification and further used for damage prognosis. To update the unknown time-invariant parameters of the FE model, two alternative stochastic filtering methods are used: the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). A three-dimensional, 5-story, 2-by-1 bay reinforced concrete (RC) frame is used to verify the proposed framework. The RC frame is modeled using fiber-section displacement-based beam-column elements with distributed plasticity and is subjected to the ground motion recorded at the Sylmar station during the 1994 Northridge earthquake. The results indicate that the proposed framework accurately estimate the unknown material parameters of the nonlinear FE model. The UKF outperforms the EKF when the relative root-mean-square error of the recorded responses are compared. In addition, the results suggest that the convergence of the estimate of modeling parameters is smoother and faster when the UKF is utilized.

Citation Download Citation

Rodrigo Astroza, Hamed Ebrahimian, and Joel P. Conte "Stochastic filtering for damage identification through nonlinear structural finite element model updating", Proc. SPIE 9435, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015, 94350T (27 March 2015); https://doi.org/10.1117/12.2084522

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KEYWORDS

3D modeling

Nonlinear filtering

Data modeling

Filtering (signal processing)

Motion models

Earthquakes

Stochastic processes

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