Site Characterization Through Hierarchical Bayesian Model Updating Using Dispersion and H/V Data

2020 ◽  
pp. 333-335
Author(s):  
Mehdi M. Akhlaghi ◽  
Mingming Song ◽  
Marshall Pontrelli ◽  
Babak Moaveni ◽  
Laurie G. Baise
Keyword(s):  
Bayesian Model ◽  
Model Updating ◽  
Site Characterization ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model

scholarly journals Hierarchical Bayesian model updating for structural identification

2015 ◽  
Vol 64-65 ◽  
pp. 360-376 ◽  
Author(s):  
Iman Behmanesh ◽  
Babak Moaveni ◽  
Geert Lombaert ◽  
Costas Papadimitriou
Keyword(s):  
Bayesian Model ◽  
Model Updating ◽  
Structural Identification ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model

scholarly journals Accounting for Modeling Errors and Inherent Structural Variability through a Hierarchical Bayesian Model Updating Approach: An Overview

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3874
Author(s):  
Mingming Song ◽  
Iman Behmanesh ◽  
Babak Moaveni ◽  
Costas Papadimitriou
Keyword(s):  
Bayesian Model ◽  
Model Updating ◽  
Bayesian Framework ◽  
Measured Data ◽  
Structural Systems ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Modeling Errors ◽  
Rc Building ◽  
And Performance

Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data. This paper provides an overview of hierarchical Bayesian model updating which has been recently developed for probabilistic integration of models with measured data, while accounting for different sources of uncertainties and modeling errors. The proposed hierarchical Bayesian framework allows one to explicitly account for pertinent sources of variability such as ambient temperatures and/or excitation amplitudes, as well as modeling errors, and therefore yields more realistic predictions. The paper reports observations from applications of hierarchical approach to three full-scale civil structural systems, namely (1) a footbridge, (2) a 10-story reinforced concrete (RC) building, and (3) a damaged 2-story RC building. The first application highlights the capability of accounting for temperature effects within the hierarchical framework, while the second application underlines the effects of considering bias for prediction error. Finally, the third application considers the effects of excitation amplitude on structural response. The findings underline the importance and capabilities of the hierarchical Bayesian framework for structural identification. Discussions of its advantages and performance over classical deterministic and Bayesian model updating methods are provided.

Robust resource demand estimation using hierarchical Bayesian model in a distributed service system

2020 ◽  
Author(s):  
Sumanta Mukherjee ◽  
Krishnasuri Narayanam ◽  
Nupur Aggarwal ◽  
Digbalay Bose ◽  
Amith Singhee
Keyword(s):  
Bayesian Model ◽  
Service System ◽  
Demand Estimation ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Resource Demand ◽  
Distributed Service
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