# A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings
## 6 Conclusions  
Modern building designs respect the required safety norms while constructing multi-story and intricate architecture, and therefore they possess less risk when subjected to seismic vulnerability. However, existing residential and commercial buildings show several signs of low construction quality, lousy maintenance, and damage signs, further increasing if associated with an earthquake.  
This study focused on the modern technique of RVS methods to analyze and minimize the risk factors associated with old and existing buildings. Machine learning is quick and cost-effective when the excellent quality of data is available. RC buildings sample data from four different countries Ecuador, Haiti, Nepal, and South Korea, are interpreted and evaluated using Machine Learning to scrutinize the damage scale for unseen samples. Eight input features, the number of stories, total floor area, column area, concrete wall area (X and Y), masonry wall area (X and Y), and captive columns are considered. The performance of the classifier is evaluated with imbalanced and balanced input data. Balanced data equally distribute the number of feature points among all the classes, by duplicating the data points from minor class, without making any significant change in the information. The classifier showed improved accuracy for each dataset with balanced data. Ecuador, Haiti, and Nepal datasets incurred an accuracy of 60%, 68%, and 67%, respectively, which illustrate that the classifier performed well over the unseen samples. The probable reason behind South Korea's limited performance (less than 50%) could be fewer sample data. A classifier does not significantly evaluate new data if less training data are available, which weakens its learning capacity.  
The future study can further extend to evaluate the SVM classifier's performance with more data while examining each feature's influence. The soft margin technique can also be implemented, replacing hyper-parameter tuning to study similar datasets for multi-class classification. Possibly k-fold cross-validation technique can also be used instead of GridSearch.  
Conceptualization, E.H. and T.L.; methodology, V.K. and K.J.; validation, T.L. and E.H.; formal analysis, E.H. and V.K.; investigation, K.J. and V.K.; resources, E.H.; data curation, V.K. and K.J.; writing--original draft preparation, E.H., K.J., V.K., and R.R.D.; writing--review and editing, E.H., K.J., V.K., and S.R.; visualization, V.K. and S.R.; supervision, E.H., T.L.; project administration, E.H. and T.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c} \hline
**Classifier** & **AUC Score (Macro)** \\ \hline Ecuador & 0.740 \\ Haiti & 0.840 \\ Nepal & 0.826 \\ South Korea & 0.775 \\ \hline \end{tabular}
\end{table}
Table 10: AUC score of classifiers.  
Figure 12: South Korea—ROC curves for each class of test dataset. Samples for classes 1 and 3 are identified in a similar trend, whereas class 3 has a maximum true positive rate achieved by the classifier. Several sample data of class 4 are taken as false positive, hence could not fulfill the threshold.  
**Funding:** This research received no external funding.  
**Acknowledgments:** We acknowledge the support of the German Research Foundation (DFG) and the Bauhaus-Universitat Weimar within the Open-Access Publishing Programme.  
**Conflicts of Interest:** The authors declare no conflict of interest.