scholarly journals An engineering approach to model-class selection for measurement-supported post-earthquake assessment

2019 ◽  
Vol 197 ◽  
pp. 109408
Author(s):  
Yves Reuland ◽  
Pierino Lestuzzi ◽  
Ian F.C. Smith
Keyword(s):  
Engineering Approach ◽  
Selection For ◽  
Model Class ◽  
Model Class Selection ◽  
Earthquake Assessment

Model Class Selection for Prediction Error Estimation

2015 ◽  
pp. 1522-1530
Author(s):  
E. Simoen ◽  
C. Papadimitriou ◽  
Geert Lombaert
Keyword(s):  
Error Estimation ◽  
Prediction Error ◽  
Selection For ◽  
Model Class ◽  
Model Class Selection

Dynamic Recursive Model Class Selection for Classifier Construction

1994 ◽  
pp. 329-337 ◽  
Author(s):  
Carla E. Brodley ◽  
Paul E. Utgoff
Keyword(s):  
Recursive Model ◽  
Selection For ◽  
Model Class ◽  
Model Class Selection

Model Class Selection for Prediction Error Estimation

2015 ◽  
pp. 1-10
Author(s):  
E. Simoen ◽  
C. Papadimitriou ◽  
G. Lombaert
Keyword(s):  
Error Estimation ◽  
Prediction Error ◽  
Selection For ◽  
Model Class ◽  
Model Class Selection

Algorithms for Bayesian Model Class Selection of Higher-Dimensional Dynamic Systems

2007 ◽  
Author(s):  
Sai Hung Cheung ◽  
James L. Beck
Keyword(s):  
Bayesian Model ◽  
Model Updating ◽  
Structural Dynamic ◽  
Higher Dimensional ◽  
Significant Probability ◽  
Model Class ◽  
Updating Procedure ◽  
Model Class Selection ◽  
Dimensional Integral ◽  
Selection Of

In recent years, Bayesian model updating techniques based on measured data have been applied in structural health monitoring. Often we are faced with the problem of how to select the ‘best’ model from a set of competing candidate model classes for the system based on data. To tackle this problem, Bayesian model class selection is used, which provides a rigorous Bayesian updating procedure to give the probability of different candidate classes for a system, based on the data from the system. There may be cases where more than one model class has significant probability and each of these will give different predictions. Bayesian model class averaging provides a coherent mechanism to incorporate all the considered model classes in the probabilistic predictions for the system. However, both Bayesian model class selection and Bayesian model class averaging require the calculation of the evidence of the model class which requires the nontrivial computation of a multi-dimensional integral. In this paper, several methods for solving this computationally challenging problem of model class selection are presented, proposed and compared. The efficiency of the proposed methods is illustrated by an example involving a structural dynamic system.

Hydrostatic-season-time model updating using Bayesian model class selection

2018 ◽  
Vol 169 ◽  
pp. 40-50 ◽  
Author(s):  
Sonja Gamse ◽  
Wan-Huan Zhou ◽  
Fang Tan ◽  
Ka-Veng Yuen ◽  
Michael Oberguggenberger
Keyword(s):  
Bayesian Model ◽  
Model Updating ◽  
Time Model ◽  
Model Class ◽  
Model Class Selection
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