Output-Only Nonlinear Finite Element Model Updating Using Autoregressive Process
| dc.coverage | DOI: 10.1007/978-3-030-47638-0_9 | |
| dc.creator | Castiglione, Juan | |
| dc.creator | Astroza, Rodrigo | |
| dc.creator | Azam, Saeed Eftekhar | |
| dc.creator | Linzell, Daniel | |
| dc.date | 2020 | |
| dc.date.accessioned | 2025-11-18T19:48:18Z | |
| dc.date.available | 2025-11-18T19:48:18Z | |
| dc.description | <p>A novel approach to deal with nonlinear system identification of civil structures subjected to unmeasured excitations is presented. Using only sparse global dynamic structural response, mechanics-based nonlinear finite element (FE) model parameters and unmeasured inputs are estimated. Unmeasured inputs are represented by a time-varying autoregressive (TAR) model. Unknown FE model parameters and TAR model parameters are jointly estimated using an unscented Kalman filter. The proposed method is validated using numerically simulated data from a 3D steel frame subjected to seismic base excitation. Six material parameters and one component of the base excitation are considered as unknowns. Excellent input and model parameter estimations are obtained, even for low order TAR models.</p> | eng |
| dc.description | A novel approach to deal with nonlinear system identification of civil structures subjected to unmeasured excitations is presented. Using only sparse global dynamic structural response, mechanics-based nonlinear finite element (FE) model parameters and unmeasured inputs are estimated. Unmeasured inputs are represented by a time-varying autoregressive (TAR) model. Unknown FE model parameters and TAR model parameters are jointly estimated using an unscented Kalman filter. The proposed method is validated using numerically simulated data from a 3D steel frame subjected to seismic base excitation. Six material parameters and one component of the base excitation are considered as unknowns. Excellent input and model parameter estimations are obtained, even for low order TAR models. © 2020, The Society for Experimental Mechanics, Inc. | spa |
| dc.identifier | https://investigadores.uandes.cl/en/publications/25033cdb-512f-46b3-865b-81e3f5f8612f | |
| dc.identifier.uri | https://repositorio.uandes.cl/handle/uandes/55496 | |
| dc.language | eng | |
| dc.publisher | Springer | |
| dc.rights | info:eu-repo/semantics/restrictedAccess | |
| dc.source | Mao, Zhu (Ed.), Model Validation and Uncertainty Quantification, Volume 3 - Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics, 2020, p.83-86. Springer. [ISBN 9783030487782] | |
| dc.subject | Auto-regressive model | |
| dc.subject | Finite element model | |
| dc.subject | Input estimation | |
| dc.subject | Kalman filter | |
| dc.subject | Model updating | |
| dc.title | Output-Only Nonlinear Finite Element Model Updating Using Autoregressive Process | eng |
| dc.type | Conference contribution | eng |
| dc.type | Contribución a la conferencia | spa |