This paper describes a novel framework that combines advanced mechanics-based nonlinear (hysteretic) finite element (FE) models and a nonlinear stochastic filtering technique, the unscented Kalman filter (UKF), to estimate unknown time-invariant parameters of nonlinear inelastic material models used in the FE model. The proposed framework updates the nonlinear FE model of the structure using input-output data recorded during earthquake events. The updated model can be directly used for damage identification. A two-dimensional 3-bay 3-story steel moment-resisting frame is used to verify the convergence, robustness, and accuracy of the proposed methodology. The steel frame is modeled using fiber-section beam-column elements with distributed plasticity and is subjected to a ground motion recorded during the 1989 Loma Prieta earthquake. The results indicate that the proposed framework provides accurate estimation of the unknown material parameters of the nonlinear FE model.
|Idioma original||Inglés estadounidense|
|Estado||Publicada - 1 ene. 2015|
|Evento||12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015 - |
Duración: 1 ene. 2015 → …
|Conferencia||12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015|
|Período||1/01/15 → …|