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.

FE Model Updating for Health Monitoring of Structures and its Experimental Verification by Damage Detection

2006 ◽  
Vol 321-323 ◽  
pp. 268-272 ◽  
Author(s):  
Gwang Hee Heo ◽  
Joon Ryong Jeon ◽  
Chin Ok Lee ◽  
Gui Lee ◽  
Woo Sang Lee
Keyword(s):  
Damage Detection ◽  
Health Monitoring ◽  
Model Updating ◽  
Basic Structure ◽  
Ambient Vibration ◽  
Fe Model ◽  
Error Matrix ◽  
Error Ratio ◽  
Before And After ◽  
Fe Model Updating

This paper presents an effective method of FE model updating for health monitoring of structures by applying ambient vibration. And this method is experimented through damage detection and proved to be valid. Experiment about ambient vibration is performed on cantilever beam, and the dynamic characteristics are analyzed by NExT and ERA. The results of such experiments are compared to those of FE analysis, and this comparison enables us to overcome some errors in experiments and analysis. On the basis of improved results by the comparison, model updating is performed in order to construct a basic structure for health monitoring. For model updating, we employ direct matrix updating method (DMUM) and Error matrix method (EMM) in which ambient vibration is easily applied. The model updating by the methods are again evaluated in terms of error ratio of natural frequency, comparing each result before and after updating. Finally, we perform experiments on damage detection to verify the method of updating presented here, and evaluate its performance by eigen-parameter change method. The evaluation proves that the method of FE model updating using ambient vibration is effective for health monitoring of structure, and some further application of this method is suggested.

scholarly journals Finite Element Model Updating in Bridge Structures Using Kriging Model and Latin Hypercube Sampling Method

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jie Wu ◽  
Quansheng Yan ◽  
Shiping Huang ◽  
Chao Zou ◽  
Jintu Zhong ◽  
...  
Keyword(s):  
Finite Element ◽  
Sampling Method ◽  
Model Updating ◽  
Structural Parameters ◽  
Latin Hypercube Sampling ◽  
Kriging Model ◽  
Computational Time ◽  
Fe Model ◽  
Latin Hypercube ◽  
Fe Model Updating

Computational cost reduction and best model updating method seeking are the key issues during model updating for different kinds of bridges. This paper presents a combined method, Kriging model and Latin hypercube sampling method, for finite element (FE) model updating. For FE model updating, the Kriging model is serving as a surrogate model, and it is a linear unbiased minimum variance estimation to the known data in a region which have similar features. To predict the relationship between the structural parameters and responses, samples are preselected, and then Latin hypercube sampling (LHS) method is applied. To verify the proposed algorithm, a truss bridge and an arch bridge are analyzed. Compared to the predicted results obtained by using a genetic algorithm, the proposed method can reduce the computational time without losing the accuracy.

A Multi-State Strategy for Structural Damage Detection Using Sensitivity of Weighted Transmissibility Function

2021 ◽  
pp. 2150144
Author(s):  
Ziwei Luo ◽  
Huanlin Liu ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Model Updating ◽  
Structural Parameters ◽  
Experimental Studies ◽  
Fe Model ◽  
Limited Information ◽  
Structural Damage Detection ◽  
State Strategy ◽  
Output Only

In practice, a model-based structural damage detection (SDD) method is helpful for locating and quantifying damages with the aid of reasonable finite element (FE) model. However, only limited information in single or two structural states is often used for model updating in existing studies, which is not reasonable enough to represent real structures. Meanwhile, as an output-only damage indicator, transmissibility function (TF) is proven to be effective for SDD, but it is not sensitive enough to change in structural parameters. Therefore, a multi-state strategy based on weighted TF (WTF) is proposed to improve sensitivity of TF to change in parameters and in order to further obtain a more reasonable FE model for SDD in this study. First, WTF is defined by TF weighted with element stiffness matrix, and relationships between WTFs and change in structural parameters are established based on sensitivity analysis. Then, a multi-state strategy is proposed to obtain multiple structural states, which is used to reasonably update the FE model and detect structural damages. Meanwhile, due to fabrication errors, a two-stage scheme is adopted to reduce the global and local discrepancy between the real structure and the FE model. Further, the [Formula: see text]-norm and the [Formula: see text]-norm regularization techniques are, respectively, introduced for both model updating and SDD problems by considering the characteristics of problems. Finally, the effectiveness of the proposed method is verified by a simply supported beam in numerical simulations and a six-storey frame in laboratory. From the simulation results, it can be seen that the sensitivity to structural damages can be improved by the definition of WTF. For the experimental studies, compared with the FE model updated from the single structural state, the FE model obtained by the multi-state strategy has an ability to more reasonably describe the change of states in the frame. Moreover, for the given structural damages, the proposed method can detect damage locations and degrees accurately, which shows the validity of the proposed method and the reliability of the updated FE model.

Sign in / Sign up

Export Citation Format

Share Document