33
| dc.contributor.advisor | 33 | |
| dc.contributor.author | Hurtado, Oscar D. | |
| dc.contributor.author | Ortíz, Albert R. | |
| dc.contributor.author | Gómez, Daniel | |
| dc.contributor.author | Astroza, Rodrigo | |
| dc.coverage | DOI: 10.1007/978-3-031-68893-5_22 | |
| dc.date | 2025 | |
| dc.date.accessioned | 05-01-2026 18:15 | |
| dc.date.available | 05-01-2026 18:15 | |
| dc.date.issued | 33 | |
| dc.description.abstract | Simplifications and theoretical assumptions are often incorporated into numerical modeling of structures; however, these assumptions may reduce the accuracy of simulation results. Model updating techniques have been developed to minimize the error between experimental response and modeled structures by updating their parameters based on observed data. Structural numerical models are typically constructed using a deterministic approach, obtaining a single best-estimated value for each structural parameter. However, structural models are often complex and involve many uncertain variables, making it impossible to find a unique solution that captures all the variability. Updating techniques using Bayesian inference (BI) have been developed to quantify parametric uncertainty in analytical models. This chapter presents the implementation of BI in the parametric updating of a five-story building model and the quantification of associated uncertainty. The Bayesian framework is implemented to update the model parameters based on experimental information provided by modal frequencies and mode shapes. The main advantage of this approach is considering the uncertainty in the experimental data, leading to a better representation of the actual building behavior. Additionally, the implications of Bayesian modeling are discussed, highlighting the importance and implications of using a multivariate normal likelihood function in the analysis. The results show that this Bayesian model updating approach effectively allows for a statistically rigorous update of model parameters, characterizing the uncertainty and increasing confidence in the model’s predictions. This is particularly useful in engineering applications where model accuracy is critical. | |
| dc.identifier | https://investigadores.uandes.cl/en/publications/897fdfba-26b7-4dde-a088-baca687cc0fa | |
| dc.identifier.citation | 33 | |
| dc.identifier.uri | 33 | |
| dc.language | eng | |
| dc.language.iso | 33 | |
| dc.publisher | Springer | |
| dc.relation | 33 | |
| dc.rights | info:eu-repo/semantics/restrictedAccess | |
| dc.source | Platz, Roland, Flynn, Garrison, Ouellette, Scott, Neal, Kyle (Ed.), Model Validation and Uncertainty Quantification, Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics 2024, p.141-146. Springer. [ISBN 9783031688928] | |
| dc.subject | Bayesian inference | |
| dc.subject | Full-scale testing | |
| dc.subject | Stochastic model updating | |
| dc.subject | Structural modeling validation | |
| dc.subject | Uncertainty quantification | |
| dc.title | 33 | |
| dc.title | Implementation of Bayesian Model Updating in Five-Story Building Using Different Observations | eng |
| dc.title | 33 | spa |
| dc.title | 33 | und |
| dc.type | 33 | |
| dc.type | Conference contribution | eng |
| dc.type | Contribución a la conferencia | spa |