# Vision-Based Structural FE Model Updating Using Genetic Algorithm
## 4 Validation Test
### FE Model Updating  
In this section, the FE model updating technique is applied to a three-story model to which BLWN input motion is applied. The natural frequency of the structure analyzed by the four object tracking algorithms based on a recording using a smartphone (Samsung GALAXXY S8) was adopted for the fitness function in the FE model updating. The FE model applied to the model updating is shown in Figure 9a, and the stiffness of the structure at the design stage and the stiffness of the damaged structure are shown in Figure 10b,c, respectively. The FE model was created based on the structure used in the lab-scale test, and it was composed of 10 nodes and 12 frame elements under a fixed boundary condition.  
Model updating was conducted by applying the results of Section 3.2 and the FE model described above. The stiffness of each floor of the structu  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Natural Frequency (Hz)**} \\ \hline
**Mode** & **KLT** & **MIL** & **KCF** & **MOSSE** \\ \hline
1 st & 1.543 & 1.550 & 1.547 & 1.541 \\ \hline
2 nd & 4.539 & 4.529 & 4.531 & 4.539 \\ \hline
3 rd & 6.726 & 6.722 & 6.724 & 6.726 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The natural frequency for different object tracking algorithms.  
Figure 10: Comparison of the estimated stiffness using different object tracking algorithms: (**a**) FE model, (**b**) column stiffness of design stage, (**c**) damaged column stiffness, and (**d-g**) estimated column stiffness using different tracking algorithms.  
model updating, and the stiffness of each floor of the structure estimated for each algorithm is shown in Table 1 and Figure 9d-g. As a result of the FE model updating, the four object tracking algorithms were confirmed to be able to search for the damaged location of the structure. This can be confirmed through the stiffness of the second floor of the structure, which was estimated to be relatively small compared to the stiffness of the other floors. For the estimated stiffness of the undamaged floor, it was confirmed that the four object tracking algorithms showed an error of approximately 5-7%. In addition, for the estimated stiffness of the damaged floor, it was confirmed that the four object tracking algorithms showed an error of approximately 18-21%. It was confirmed that the damaged floor of the structure showed a relatively low accuracy compared to the undamaged floor. However, owing to the characteristics of the finite element model, the lab-scale model cannot be fully simulated; therefore, an error of approximately 20% is considered to be a fairly reliable result. It was confirmed that the estimated stiffness of the structure using the four object tracking algorithms showed a similar accuracy. This is because the value of the natural frequency (Table 3), which are the dynamic characteristics of the structure estimated by the four object tracking algorithms, have similar values. However, when only the estimated stiffness of the damaged structure was considered, it was confirmed that the most appropriate algorithm to be applied to the FE model updating is the KLT algorithm.  
Although the natural frequency (Table 2) of the structure predicted by the four object tracking algorithms showed similar values, it was confirmed that the accuracy of the estimated stiffness of the structure differs by floor. Based on the above results, it was confirmed that when FE model updating will be performed using the natural frequency of the structure as the fitness function, the accuracy of the FE model updating may be lowered if the highly accurate natural frequency of the structure is not applied.