A FE model updating technique based on SAP2000-OAPI and enhanced SOS algorithm for damage assessment of full-scale structures

2020 ◽  
Vol 89 ◽  
pp. 106100 ◽  
Author(s):  
D. Dinh-Cong ◽  
T. Nguyen-Thoi ◽  
Duc T. Nguyen
2002 ◽  
Vol 80 (25) ◽  
pp. 1869-1879 ◽  
Author(s):  
Anne Teughels ◽  
Johan Maeck ◽  
Guido De Roeck

2018 ◽  
Vol 18 (08) ◽  
pp. 1840004 ◽  
Author(s):  
Tianyi Zhu ◽  
Wei Tian ◽  
Shun Weng ◽  
Hanbin Ge ◽  
Yong Xia ◽  
...  

An accurate finite element (FE) model is frequently used in damage detection, optimization design, reliability analysis, nonlinear analysis, and so forth. The FE model updating of large-scale structures is usually time-consuming or even impossible. This paper proposes a dynamic condensation approach for model updating of large-scale structures. The eigensolutions are calculated from a condensed eigenequation and the eigensensitivities are calculated without selection of additional master DOFs, which is helpful to improve the efficiency of FE model updating. The proposed model updating method is applied to an eight-storey frame and the Jun Shan Yangtze Bridge. By employing the dynamic condensation approach, the number of iterations for the eigensensitivities is gradually increased according to the model updating process, which contributes to accelerate the convergence of model updating.


2021 ◽  
Vol 11 (4) ◽  
pp. 1622
Author(s):  
Gun Park ◽  
Ki-Nam Hong ◽  
Hyungchul Yoon

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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