## 6 Discussion and Conclusions
### Applicability of ML-Based Methods to RVS Purposes  
The study concludes that the assessed vulnerability classes were very close to the actual damage levels observed in the buildings, which, in comparison to previous methods, provided a more reliable distribution between different damage levels. The proposed method is more accurate than other methods as this rate shows a significant improvement of about 2% to 35% compared to methods stated previously by [5; 15; 17; 18; 27]. However, supervised ML techniques are data dependent, and the scope of this study was on RC buildings. The selected parameters for this evaluation are also more comfortable to be measured; moreover, they can be modified based on the availability or demands of experts. Therefore, the application of the proposed method can be adapted for other countries in the world but need to give specific or different types of buildings by providing enough data for training from the selected location. Nevertheless, besides financial benefits and better natural disaster management planning, this achievement also makes building retrofitting more intelligent and saves its residents' lives. It must be mentioned that it is not expected from RVS methods to have high accuracy and an exact estimate of the possible damage. Since the proposed method is based on MCE, it can be implemented for each studied region and building type. In reality, an overwhelming number of factors and significant parameters play roles in the vulnerability of a building. The main aim of the RVS method is to obtain acceptable classification and initial assessment of damage and be prepared to prevent catastrophe. Benefits such as proper budget allocation and prioritization of building retrofitting and providing measures to prevent buildings and occupants' damage benefit this method. Moreover, in future studies, more data and parameters such asdifferent structural types, soil conditions, distance to faults, age of buildings, and remote sensing data can be considered.  
Conceptualization, E.H. and V.K.; methodology, E.H. and V.K.; validation, T.L. and E.H.; formal analysis, E.H. and V.K.; investigation, K.J. and V.K.; resources, E.H.; data curation, K.J. and V.K.; writing--original draft preparation, E.H., K.J., V.K., S.R. and R.R.D.; writing--review and editing, E.H., K.J., V.K. and S.R.; visualization, E.H. and V.K.; supervision, E.H. and T.L.; project administration, E.H. and T.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We acknowledge the support of the German Research Foundation (DFG) and the Bauhaus-Universitat Weimar within the Open-Access Publishing Program. In addition, the ShakeMap and materials are courtesy of the U.S. Geological Survey.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} \multicolumn{2}{l}{ACI} & \multicolumn{1}{l}{American Concrete Institute} \\ ANN & Artificial Neural Network \\ AUC & Area under the Curve \\ ET & Extra Tree \\ ETE & Extra Tree Ensemble \\ ESPOL & Escuela Superior Politecnica del Litoral \\ FEMA & Federal Emergency Management Agency \\ KNN & K-Nearest Neighbor \\ MCE & Maximum Considered Earthquake \\ MM & Modified Mercalli \\ ML & Machine Learning \\ RC & Reinforced Concrete \\ RF & Random Forest \\ ROC & Receiver Operating Characteristics \\ RVS & Rapid Visual Screening \\ SVA & Seismic Vulnerability Assessment \\ SVM & Support Vector Machine \\ \end{tabular}