scholarly journals A Sequential Framework for Improving Identifiability of FE Model Updating using Static and Dynamic Data

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5099 ◽  
Author(s):  
Kim ◽  
Kim ◽  
Park ◽  
Jin
Keyword(s):  
Experimental Study ◽  
Objective Function ◽  
Model Updating ◽  
Heterogeneous Data ◽  
Fe Model ◽  
Validation Data ◽  
Dynamic Data ◽  
Parameter Identifiability ◽  
Fe Model Updating ◽  
Parameter Compensation

By virtue of the advances in sensing techniques, finite element (FE) model updating (FEMU) using static and dynamic data has been recently employed to improve identification on updating parameters. Using heterogeneous data can provide useful information to improve parameter identifiability in FEMU. It is worth noting that the useful information from the heterogeneous data may be diluted in the conventional FEM framework. The conventional FEMU framework in previous studies have used heterogeneous data at once to compute residuals in the objective function, and they are condensed to be a scalar. In this implementation, it should be careful to formulate the objective function with proper weighting factors to consider the scale of measurement and relative significances. Otherwise, the information from heterogeneous data cannot be efficiently utilized. For FEMU of the bridge, parameter compensation may exist due to mutual dependence among updating parameters. This aggravates the parameter identifiability to make the results of the FEMU worse. To address the limitation of the conventional FEMU method, this study proposes a sequential framework for the FEMU of existing bridges. The proposed FEMU method uses two steps to utilize static and dynamic data in a sequential manner. By using them separately, the influence of the parameter compensation can be suppressed. The proposed FEMU method is verified through numerical and experimental study. Through these verifications, the limitation of the conventional FEMU method is investigated in terms of parameter identifiability and predictive performance. The proposed FEMU method shows much smaller variabilities in the updating parameters than the conventional one by providing the better predictions than those of the conventional one in calibration and validation data. Based on numerical and experimental study, the proposed FEMU method can improve the parameter identifiability using the heterogeneous data and it seems to be promising and efficient framework for FEMU of the existing bridge.

The Application of Bees Algorithm in Finite Element Model Updating

2010 ◽  
Author(s):  
Laleh Fatahi ◽  
Shapour Moradi ◽  
Pejman Razi
Keyword(s):  
Finite Element ◽  
Objective Function ◽  
Model Updating ◽  
Research Work ◽  
Modal Testing ◽  
Bees Algorithm ◽  
Physical Parameters ◽  
Piping System ◽  
Fe Model ◽  
Fe Model Updating

This research work is aimed to investigate the application of bees algorithm (BA) to the finite element (FE) model updating. BA is an evolutionary optimization algorithm that imitates the natural foraging behavior of the honeybees to find the global optimum of an objective function. Here, the weighted squared sum of the error between the measured modal parameters and the FE model predictions is considered as the objective function. To demonstrate the effectiveness of the proposed method, BA is applied on a piping system to update several physical parameters of its FE model. The results obtained from the numerical model are compared with the experimental ones obtained through the modal testing. The results show that BA successfully updates the FE model. Moreover, the performance of this approach is compared with two popular optimization methods; the genetic algorithm (GA) and the particle swarm optimization (PSO). The comparison shows the advantage of BA over GA and its similarity to PSO in terms of accuracy in the presented case study. However, BA reaches to the optimum solution faster than PSO and GA. Therefore, it can be concluded that BA is a robust and accurate optimization method that could be a good candidate for the 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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