# Vision-Based Structural FE Model Updating Using Genetic Algorithm
## 5 Discussion  
This paper presented a new technique to perform the FE model updating of the structures using vision-based displacement measurements. Although the proposed method achieved reliable results for updating the FE model into a damaged state, there were some errors owing to the following reasons.  
1. One of the main sources of the error is a modeling error. The FE model used in this study was a frame structure. Detailed information such as bolt connections was not considered in this model. Therefore, the response owing to these connections was not fully reflected in the updated model. The accuracy of the FE model updating is expected to be improved by developing a more detailed FE model.
2. Another source of error is inaccurate damping. The damping ratio of the structure is an important factor influencing the response of the structure. In practice, the damping ratio is assumed to be 5% for reinforced concrete structures and 2% for welded steel structures but can be varied by the material and structural conditions. [PERSON] et al. [38] compared the modal damping ratio obtained by considering the measurement duration, frequency range, and sampling rate, with the method used to identify the modal parameters as variable parameters. As a result, it was observed  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \begin{tabular}{c} **Design** \\ **Stage** \\ **(N/mm)** \\ \end{tabular} & \begin{tabular}{c} **Damaged** \\ **(N/mm)** \\ \end{tabular} & \multicolumn{5}{c}{**Analysis (N/mm)**} & \multicolumn{5}{c}{**Error (\%)**} \\ \cline{3-10}  & \begin{tabular}{c} **Stage** \\ **(N/mm)** \\ \end{tabular} & \begin{tabular}{c} **KLT** \\ \end{tabular} & \begin{tabular}{c} **MIL** \\ \end{tabular} & \begin{tabular}{c} **KCF** \\ \end{tabular} & \begin{tabular}{c} **MOSSE** \\ \end{tabular} & \begin{tabular}{c} **KLT** \\ \end{tabular} & \begin{tabular}{c} **MIL** \\ \end{tabular} & \
\begin{tabular}{c} **KCF** \\ \end{tabular} &
\begin{tabular}{c} **MOSSE** \\ \end{tabular} \\ \hline Floor 1 & 1,286,400 & 1,286,400 & 1,194,896 & 1,211,896 & 1,211,896 & 1,217,792 & 6.833 & 5.792 & 5.792 & 5.333 \\ \hline Floor 2 & 1,286,400 & 1,270,320 & 1,031,800 & 995,888 & 993,208 & 983,024 & 18.776 & 21.603 & 21.814 & 22.616 \\ \hline Floor 3 & 1,286,400 & 1,286,400 & 1,189,384 & 1,194,744 & 1,199,568 & 1,188,848 & 7.542 & 7.125 & 6.75 & 7.583 \\ \hline Average & - & - & - & - & - & - & 11.050 & 11.507 & 11.452 & 11.844 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of column stiffness between experiment and prediction model using different tracking algorithms.  
that the change in natural frequency was extremely small whereas the modal damping ratio changed significantly depending on the selected parameters. In this study, the damping ratio of the structure was assumed to be 2%, which caused an error in the FE model updating.
3. Imperfection in vision-based tracking algorithms can also be a source of error. In this study, displacement was measured using four tracking methods, and FE model updating was performed using the results. However, the results of the model updating showed differences according to each tracking method. Each tracking algorithm has its own tracking mechanisms that can cause errors during the tracking process. Some common phenomena in computer vision, such as radial distortion and a rolling shutter effect, have also reduced the accuracy. In this study, these issues were minimized using the method proposed by [PERSON] et al. [39], but these distortions could not be fully removed.