Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Bayesian Updating of Structural Model with a Conditionally Heteroscedastic Error Distribution
The existing literature on Bayesian updating of structural models has assigned equal variances (homoscedasticity) in the measured observables across all modes by assuming a Gaussian error distribution. This paper relaxes the assumption by allowing the error distribution to be conditionally heteroscedastic but marginally follow the Student’s -distribution. Modeling heteroscedasticity in structures is necessary because higher modal parameters are prone to higher uncertainty—an idea that is supported by experimental data obtained from a scaled building model. We adopted a hierarchical modeling framework for system equations and employed Bayesian techniques to update the parameters. Estimation used Gibbs sampling, a well-known Markov chain Monte Carlo (MCMC) algorithm. The proposed framework is illustrated in various simulated data obtained from a 10-story shear building for varying levels of noise contamination. The model was also implemented for experimental data from an eight-degrees-of-freedom spring-mass model tested at the Los Alamos National Laboratory. We show that our framework provides several advantages over the homoscedastic model, including a more stable and accurate inference about the stiffness and modal parameters and a noticeable improvement in noise immunity compared to those reported in the literature. The importance of the heteroscedasticity is also highlighted because it leads to a better exploration of the associated uncertainty in the observables, not captured by its homoscedastic counterpart. The paper also discusses some limitations and provides possible extensions for future research.
Bayesian Updating of Structural Model with a Conditionally Heteroscedastic Error Distribution
The existing literature on Bayesian updating of structural models has assigned equal variances (homoscedasticity) in the measured observables across all modes by assuming a Gaussian error distribution. This paper relaxes the assumption by allowing the error distribution to be conditionally heteroscedastic but marginally follow the Student’s -distribution. Modeling heteroscedasticity in structures is necessary because higher modal parameters are prone to higher uncertainty—an idea that is supported by experimental data obtained from a scaled building model. We adopted a hierarchical modeling framework for system equations and employed Bayesian techniques to update the parameters. Estimation used Gibbs sampling, a well-known Markov chain Monte Carlo (MCMC) algorithm. The proposed framework is illustrated in various simulated data obtained from a 10-story shear building for varying levels of noise contamination. The model was also implemented for experimental data from an eight-degrees-of-freedom spring-mass model tested at the Los Alamos National Laboratory. We show that our framework provides several advantages over the homoscedastic model, including a more stable and accurate inference about the stiffness and modal parameters and a noticeable improvement in noise immunity compared to those reported in the literature. The importance of the heteroscedasticity is also highlighted because it leads to a better exploration of the associated uncertainty in the observables, not captured by its homoscedastic counterpart. The paper also discusses some limitations and provides possible extensions for future research.
Bayesian Updating of Structural Model with a Conditionally Heteroscedastic Error Distribution
Lyngdoh, Gideon Arthur (Autor:in) / Rahman, Mohammad Arshad (Autor:in) / Mishra, Sudib Kumar (Autor:in)
17.09.2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
| Wiley | 2025
Bayesian Updating with Structural Reliability Methods
| ASCE | 2014
Bayesian Updating with Structural Reliability Methods
| Online Contents | 2015