Bayesian Model-Updating Implementation in a Five-Story Building
Simplifications and theoretical assumptions are usually incorporated into the numerical modeling of structures. However, these assumptions may reduce the accuracy of the simulation results. This problem has led to the development of model-updating techniques to minimize the error between the experim...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/13/6/1568 |
| _version_ | 1827738081232420864 |
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| author | Oscar D. Hurtado Albert R. Ortiz Daniel Gomez Rodrigo Astroza |
| author_facet | Oscar D. Hurtado Albert R. Ortiz Daniel Gomez Rodrigo Astroza |
| author_sort | Oscar D. Hurtado |
| collection | DOAJ |
| description | Simplifications and theoretical assumptions are usually incorporated into the numerical modeling of structures. However, these assumptions may reduce the accuracy of the simulation results. This problem has led to the development of model-updating techniques to minimize the error between the experimental response and the modeled structure by updating its parameters based on the observed data. Structural numerical models are typically constructed using a deterministic approach, whereby a single best-estimated value of each structural parameter is obtained. However, structural models are often complex and involve many uncertain variables, where a unique solution that captures all the variability is not possible. Updating techniques using Bayesian Inference (BI) have been developed to quantify parametric uncertainty in analytical models. This paper presents the implementation of the BI in the parametric updating of a five-story building model and the quantification of its associated uncertainty. The Bayesian framework is implemented to update the model parameters and calculate the covariance matrix of the output parameters based on the experimental information provided by modal frequencies and mode shapes. The main advantage of this approach is that the uncertainty in the experimental data is considered by defining the likelihood function as a multivariate normal distribution, leading to a better representation of the actual building behavior. The results showed that this Bayesian model-updating approach effectively allows a statistically rigorous update of the model parameters, characterizing the uncertainty and increasing confidence in the model’s predictions, which is particularly useful in engineering applications where model accuracy is critical. |
| first_indexed | 2024-03-11T02:39:30Z |
| format | Article |
| id | doaj.art-222d43dec61f451b8a45ea7afb0b7f97 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-11T02:39:30Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-222d43dec61f451b8a45ea7afb0b7f972023-11-18T09:40:02ZengMDPI AGBuildings2075-53092023-06-01136156810.3390/buildings13061568Bayesian Model-Updating Implementation in a Five-Story BuildingOscar D. Hurtado0Albert R. Ortiz1Daniel Gomez2Rodrigo Astroza3School of Civil Engineering and Geomatics, Universidad del Valle, Cali 760032, ColombiaSchool of Civil Engineering and Geomatics, Universidad del Valle, Cali 760032, ColombiaSchool of Civil Engineering and Geomatics, Universidad del Valle, Cali 760032, ColombiaFacultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago 7620001, ChileSimplifications and theoretical assumptions are usually incorporated into the numerical modeling of structures. However, these assumptions may reduce the accuracy of the simulation results. This problem has led to the development of model-updating techniques to minimize the error between the experimental response and the modeled structure by updating its parameters based on the observed data. Structural numerical models are typically constructed using a deterministic approach, whereby a single best-estimated value of each structural parameter is obtained. However, structural models are often complex and involve many uncertain variables, where a unique solution that captures all the variability is not possible. Updating techniques using Bayesian Inference (BI) have been developed to quantify parametric uncertainty in analytical models. This paper presents the implementation of the BI in the parametric updating of a five-story building model and the quantification of its associated uncertainty. The Bayesian framework is implemented to update the model parameters and calculate the covariance matrix of the output parameters based on the experimental information provided by modal frequencies and mode shapes. The main advantage of this approach is that the uncertainty in the experimental data is considered by defining the likelihood function as a multivariate normal distribution, leading to a better representation of the actual building behavior. The results showed that this Bayesian model-updating approach effectively allows a statistically rigorous update of the model parameters, characterizing the uncertainty and increasing confidence in the model’s predictions, which is particularly useful in engineering applications where model accuracy is critical.https://www.mdpi.com/2075-5309/13/6/1568modal analysisBayesian inferenceparametric uncertaintyprobabilistic model updatingfull-scale testingfinite element modeling |
| spellingShingle | Oscar D. Hurtado Albert R. Ortiz Daniel Gomez Rodrigo Astroza Bayesian Model-Updating Implementation in a Five-Story Building Buildings modal analysis Bayesian inference parametric uncertainty probabilistic model updating full-scale testing finite element modeling |
| title | Bayesian Model-Updating Implementation in a Five-Story Building |
| title_full | Bayesian Model-Updating Implementation in a Five-Story Building |
| title_fullStr | Bayesian Model-Updating Implementation in a Five-Story Building |
| title_full_unstemmed | Bayesian Model-Updating Implementation in a Five-Story Building |
| title_short | Bayesian Model-Updating Implementation in a Five-Story Building |
| title_sort | bayesian model updating implementation in a five story building |
| topic | modal analysis Bayesian inference parametric uncertainty probabilistic model updating full-scale testing finite element modeling |
| url | https://www.mdpi.com/2075-5309/13/6/1568 |
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