## 4 Summary and Conclusions  
In this research, we have employed a novel machine learning methodology to approach one of the problems commonly found in countries with high seismic activity, namely, that of the preseismic structural assessment. Specifically, we performed an analysis of how the features obtained in the Rapid Visual Screening procedure affect the seismic vulnerability of structures. We specificallyfocused on three well-known damage thresholds: the Serviceability Limit State, the Ultimate Limit State, and the Collapse Limit State, to further emphasize structures that, in addition to crossing the ULS, suffered total or partial collapse. We employed a pairwise approach to perform our analysis, creating pairs from all structures belonging to adjacent damage categories, as shown in Table 1. We then used a Gradient Boosting Machine to create a binary classification model that learned to distinguish structures for each of the above damage thresholds. As shown in Table 2, we tuned some of the model's hyperparameters to increase its performance. This led to the model having high accuracy, especially in the higher damage categories.  
As can be seen from Table 3, the model learned to distinguish the CLS threshold with almost 92% accuracy; similarly, for the ULS threshold it displayed an accuracy close to 89%. While the model's performance dropped to 73% for the SLS, this is the least impactful of the three damage thresholds in engineering practice. Finally, we used SHAP values to quantify the effect of each of the features in our models' predictions. The previously mentioned high accuracy of our models, especially in the higher damage categories, enhances the reliability of the subsequently extracted SHAP values.  
In addition, the present study highlights the participation of various factors that contribute to the overall structural vulnerability index as calculated via the RSVP. Qualitatively, our results broadly agree with the previously established engineering literature. For the CLS threshold, soft storeys/short columns, the height of the structure, absence of design seismic codes, and irregularities in height and plan were the most impactful detrimental factors. Regular infill wall panels were shown to have a very positive effect. For the ULS threshold, the absence of a design seismic code did not have a significant influence, as the vast majority of structures with this feature that crossed the ULS crossed the CLS as well. Finally, the implementation of modern design seismic codes played a crucial role in preventing structures from crossing the SLS threshold.  
The quantitative results obtained via the application of ML methods and SHAP values demonstrates the potential applicability of this approach for recalibrating the computation of structural vulnerability indices using data from recent earthquakes. The method implemented in the present paper pertains to reinforced concrete structures with a particular set of input features; however, it could be implemented in an identical manner using a different set of input features, for example, in countries where other parameters are deemed more important. It could also be employed in different structural types altogether, for example, in masonry buildings commonly found in traditional communities.  
Conceptualization, I.K. and A.K.; methodology, I.K., L.I. and A.K.; software, I.K., L.I. and A.K.; validation, I.K., L.I. and A.K.; formal analysis, I.K., L.I. and A.K.; investigation, I.K., L.I. and A.K.; resources, A.K.; data curation, I.K., L.I. and A.K.; writing--original draft preparation, I.K. and A.K.; writing--review and editing, L.I.; visualization, I.K.; supervision L.I. and A.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset employed in this study can be made available upon reasonable request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
R.R.R.R.R.R.R.R.R.R.R.R.R.R.R.R.R.R.R.R.R