## 1 Introduction  
During the last decades, due to the large amount of existing building stock, engineering focus has shifted from analyzing and designing new structures to maintaining preexisting buildings to modern standards of safety and serviceability [1]. As is well known, the results of an earthquake can be catastrophic to society in terms of loss of human lives and require large monetary preparations, with examples including the Turkey (Izmit) 1999, Athens 1999, Pakistan 2005, and Turkey 2023 earthquakes. Governments and authorities can take preemptive measures to mitigate these effects; however, due to obvious limitations in resources and manpower, it is not possible to do so for all existing buildings, especially in large urban areas. Thus, most countries have introduced multi-stage procedures to assess and evaluate the total potential consequences and losses from an earthquake, and thereby identify the most critical structures where allocation of further resources should be prioritized. As a first step in these methods a Rapid Visual Screening Procedure (RVSP) [2] is usually performed, wherein experts quickly inspect buildings and identify key structural characteristics that affect the overall seismic behaviour. For example, this could include whether or not the structure has short columns or soft storeys, the presence of neighboring buildings that could result in bounding effects, irregularities in the horizontal or vertical plan of the building, and others [3, 4]. Subsequently, these obtained characteristics are weighted to compute a seismic vulnerability index which is used to rank the structures according to their expected degree of damage [5]. Finally, the most vulnerable structures that have been identified from the aforementioned steps are subjected to more accurate analytical methods such as step-by-step dynamic analysis. These methods take into account other structural characteristics, such as the design of structural reinforcement and quality of concrete, and yield an accurate assessment of the seismic vulnerability of the structures under consideration. In turn, this allows for the identification of any potentially required preemptive measures to be applied. However, they are prohibitively costly and time consuming to apply to every structure in the population. In USA, the Federal Emergency Management Agency (FEMA) first introduced such an RVSP [2] in 1988, which has since been modified to include more structural features that affect the overall seismic performance [6]. Countries with high seismic activity, such as Japan, Italy, Canada, India, and Greece, have derived similar pre-earthquake assessments adapted to the characteristics of their respective building stocks. The success of RVSP in screening candidate structures for further analysis heavily depends on accurate calibration of the weights of the structural characteristics. Thus, past researchers have used data from major recorded earthquakes in conjunction with engineering expertise for this task [7; 8]. Similarly, for masonry buildings, both index-based [9] and physics-based [10] structural vulnerability assessment studies have been conducted. The effect of the structural parameters of this type of building on their structural vulnerability has been studied as well [11; 12]. On the other hand, recent years have seen an increase in the use of Machine Learning (ML) methods for the task of predicting the degree of damage of reinforced concrete structures. Classification techniques have been previously employed to classify structures into predicted damage categories. [PERSON] et al. [13] employed Support Vector Machines, which they calibrated on dataset of earthquakes in four different countries. [PERSON] et al. [14] employed a variety of models, including Decision Trees, Random Forests, XGBoost, and Logistic Regression. Similarly, regression methods have been employed for this task. Among others, [PERSON] [15] and [PERSON] et al. [16] used ML methods to predict the instrostery drift, which can be used as a damage index.