# Investigation of Structural Seismic Vulnerability Using Machine Learning on Rapid Visual Screening
## 4 Summary and Conclusions  
In the framework of the present study, we have utilized ML-based methodologies to address one of the most severe and impactful engineering challenges; the pre-earthquake seismic vulnerability ranking of a large number of structures within a building stock. As was examined, different countries and organizations have proposed similar yet distinct methodologies. We have examined the approach employed by FEMA (as formulated in 1988 and revised in 2002), India, Canada, New Zealand, and the Greek OASP. As it was shown in Table 2, a different set of features is employed by each of the aforementioned Codes. Even when the same features are used, different weights can be assigned to each of them, based on experts' opinions. To mitigate this, we have employed a ML-based pairwise methodology, specifically targeting the problem of ranking. More specifically, the Gradient Boosting Machine algorithm was employed to perform comparisons between pairs of structures. As it can be seen from Tables 3 and 4, as well as from Figure 2a,b, the proposed methodology was able to outperform currently established engineering practices and to improve on the results obtained with each separate Seismic Code. Regarding the structures that crossed CLS (\"Black\"), all the examined ML models exhibited an accuracy of over 50%. In some cases, like FEMA 2022 or New Zealand's Code, we have achieved an accuracy increase between 10 and 20%, compared to the ranking results obtained by the use of the respective Codes' coefficients. The accuracy improvement for the structures that crossed the ULS (\"Red\") was as high as 15% for most of the examined Codes. The performance improved for the other two damage labels as well (moderate--\"Yellow\", minimal--\"Green\"), although these are less impactful than the first two. The above highlight that Gradient Boosting was able to adapt better to the available features and to produce ranking results with higher accuracy and demonstrate its applicability for the task of seismic vulnerability ranking. Subsequently, the proposed methodology was extended using ML feature attribution techniques, as examined in Sections 2.5 and 2.6. Inspired by the linearity in which the examined Codes compute their RVS scores, we proposed a linearization of the fully trained ML models using the aggregated metrics of the so-called SHAP values to produce weights similar to the ones employed in a standard RVS procedure. As these values are designed to approximate the behavior of the underlying ML model, we observed that, in most cases, employing this recalibrated and reformulated RVS methodology retained increased performance metrics, compared to the respective currently established approaches. The benefit of the proposed formulation is that the trained ML models are capable of learning complex, non-linear patterns inherent in the data, which in turn leads to their ability to outperform currently established engineering practices of different countries and organizations. Furthermore, the proposed linearization procedure is also based on the fully trained ML models via the SHAP values feature attribution technique. Thus, even though it inspired by the currently established engineering practices, it provides a basis with which they can be recalibrated to obtain increased accuracy. Our case study was based on a dataset consisting of real measurements from 457 reinforced concrete structures obtained after the 1999 Athens earthquake. However, given the promising results that were obtained, the proposed methodologies could be expanded. Indeed, different countries and organizations employ different features of interest for pre-earthquake seismic vulnerability ranking in an RVS context. In addition, buildings with different structural types, e.g., masonry buildings commonly found in traditional communities, employ a fundamentally different set of features.