# Investigation of Structural Seismic Vulnerability Using Machine Learning on Rapid Visual Screening
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Seismic Code**} & \multicolumn{4}{c}{**Bin Accuracy (\%)**} \\ \cline{2-5}  & **Black** & **Red** & **Yellow** & **Green** \\ \hline FEMA 1988 & 52.69 & 41.00 & 30.43 & 41.94 \\ FEMA 2002 & 61.29 & 38.00 & 26.09 & 30.64 \\ OASP & 53.76 & 37.00 & 28.99 & 46.77 \\ (2000 and 2004) & & & & \\ India & 56.99 & 41.00 & 24.66 & 29.03 \\ Canada & 55.94 & 42.00 & 28.99 & 35.48 \\ New Zealand & 53.76 & 38.00 & 31.88 & 45.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Bin accuracy obtained using the fully trained Machine Learning model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Seismic Code**} & \multicolumn{4}{c}{**Bin Accuracy (\%)**} \\ \cline{2-5}  & **Black** & **Red** & **Yellow** & **Green** \\ \hline FEMA 1988 & 24.73 & 22.00 & 5.80 & 22.58 \\ FEMA 2002 & 37.63 & 24.00 & 11.59 & 19.35 \\ OASP 2000 & 43.01 & 32.00 & 14.49 & 27.42 \\ OASP 2004 & 48.39 & 35.00 & 24.64 & 32.26 \\ India & 33.33 & 24.00 & 18.84 & 19.35 \\ Canada & 36.56 & 31.00 & 14.49 & 16.13 \\ New Zealand & 38.71 & 23.00 & 30.43 & 12.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bin accuracy obtained using the established RVS scores. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Seismic Code**} & \multicolumn{4}{c}{**Bin Accuracy (\%)**} \\ \cline{2-5}  & **Black** & **Red** & **Yellow** & **Green** \\ \hline FEMA 1988 & 56.99 & 44.00 & 30.43 & 19.35 \\ FEMA 2002 & 60.22 & 37.00 & 27.54 & 33.87 \\ OASP & 53.76 & 32.00 & 21.74 & 30.65 \\ India & 53.76 & 30.00 & 27.54 & 30.65 \\ Canada & 37.63 & 24.00 & 20.29 & 25.81 \\ New Zealand & 46.24 & 39.00 & 30.43 & 19.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Bin accuracy obtained using the proposed recalibrated RVS. We have compared the rankings obtained after running the fully trained Machine Learning models for all the examined Seismic Codes and all the damage categories. We can readily observe that there is a significant increase in the accuracy in all cases. More specifically, the accuracy for the Black class (worst case) was higher than 50% for all employed models. FEMA 2002 achieved a significant accuracy as high as 61%. It should be noted that the same Seismic Code in its current form achieved an accuracy of slightly higher accuracy. Figure 2: Comparison of the obtained bin accuracies for the examined Seismic Codes and methodologies. (**a**) Ranking obtained using RVS. (**b**) Ranking obtained using the fully trained Machine Learning models. (**c**) Ranking obtained using the proposed recalibrated RVS. less than 40% without the use of ML classification algorithms. The models based on the Codes of India, Canada, and New Zealand also exhibited performance improvement. Their accuracy for the Black class bin increased more than 10% with the use of the ML models. OASP exhibited a slight increase of approximately 5% in the accuracy of the Black bin. The highest performance increase for this Code was achieved for the Green class bin, corresponding to buildings that suffered minimal damages. For the Red bin (severe damages), the second most impactful, almost all Seismic Codes exhibited an increase close to 15%. The OASP Code seemed to have benefited the least in this bin as well, with an increase of only 5%. However, it should be reiterated that OASP's performance was the highest to begin with. Finally, the bin accuracy of the Black label in FEMA 1988 exhibited a noteworthy increase, from approximately 25% to more than 50% when the fully trained ML model was employed. All of the above clearly indicate the significant benefit of employing ML models for the task of seismic vulnerability ranking. As was demonstrated, using the same features, ML models are capable of learning complex, non-linear relationships that significantly outperform currently established engineering practices. Finally, we compared the rankings obtained using the proposed ML-based recalibration procedure for RVS.