scholarly journals Uncertainty quantification in the damage assessment of a seven-story RC building slice through Bayesian FE model updating

2014 ◽  
pp. 1955-1962
Author(s):  
E Simoen ◽  
B Moaveni ◽  
J Conte ◽  
G Lombaert
Keyword(s):  
Uncertainty Quantification ◽  
Damage Assessment ◽  
Model Updating ◽  
Fe Model ◽  
Rc Building ◽  
Fe Model Updating

Damage assessment by FE model updating using damage functions

2002 ◽  
Vol 80 (25) ◽  
pp. 1869-1879 ◽  
Author(s):  
Anne Teughels ◽  
Johan Maeck ◽  
Guido De Roeck
Keyword(s):  
Damage Assessment ◽  
Model Updating ◽  
Fe Model ◽  
Damage Functions ◽  
Fe Model Updating

scholarly journals Vision-Based Structural FE Model Updating Using Genetic Algorithm

2021 ◽  
Vol 11 (4) ◽  
pp. 1622
Author(s):  
Gun Park ◽  
Ki-Nam Hong ◽  
Hyungchul Yoon
Keyword(s):  
Genetic Algorithm ◽  
Model Updating ◽  
Structural Parameters ◽  
Fe Model ◽  
Structural Members ◽  
Stiffness Reduction ◽  
Before And After ◽  
Scale Test ◽  
Fe Model Updating ◽  
Story Building

Structural members can be damaged from earthquakes or deterioration. The finite element (FE) model of a structure should be updated to reflect the damage conditions. If the stiffness reduction is ignored, the analysis results will be unreliable. Conventional FE model updating techniques measure the structure response with accelerometers to update the FE model. However, accelerometers can measure the response only where the sensor is installed. This paper introduces a new computer-vision based method for structural FE model updating using genetic algorithm. The system measures the displacement of the structure using seven different object tracking algorithms, and optimizes the structural parameters using genetic algorithm. To validate the performance, a lab-scale test with a three-story building was conducted. The displacement of each story of the building was measured before and after reducing the stiffness of one column. Genetic algorithm automatically optimized the non-damaged state of the FE model to the damaged state. The proposed method successfully updated the FE model to the damaged state. The proposed method is expected to reduce the time and cost of FE model updating.

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