## 1 Introduction  
The vast majority of economic and human losses resulting from earthquakes are a product of poor construction efforts carried out by humans [1; 2; 3; 4]. In this context, vulnerability assessment provides an essential tool for understanding the geographical distribution of seismic risk and for the development and implementation of risk mitigation strategies [5]. One of the main components considered inthe majority of risk assessment methodologies is the relationship between the danger intensity parameter and the level of damage or loss for a specific building classification [6; 7]. Generally, two different approaches are employed, one of which is based on fragility functions, and the other on vulnerability functions. In order to advance in the formulation of fragility and vulnerability functions, methods have been developed that encompass empirical and analytical approaches. Empirical methods are based on research conducted in laboratory tests [7] and data collected from observations of actual damages that occurred after previous earthquakes. These methods involve statistical data analysis for calibration [3]. However, they present certain limitations, such as subjectivity in assigning each building to a damage state and a lack of precision in determining the ground motion impacting the region [8]. Among the empirical methods, several stand out, such as the Damage Probability Matrix (DPM) method [9] for vulnerability assessment, the Vulnerability Index Method (VIM),which is like the Italian method developed by [PERSON] et al. [10], and the macroseismic method [11]; these are used to establish fragility and vulnerability functions. Additionally, there are methods, like the Agibilita e Dannonell'EmergenzaSismica (AeDES) method [12] and the Rapid Visual Assessment (RVS) method described in FEMA [13], both of which are used to determine damage levels in structures. Recently, a study on unreinforced masonry residential buildings was conducted by [PERSON] et al. [14], in which they presented an empirically derived fragility model. This model was obtained through statistical data processing of damage data collected following previous seismic events in the municipalities of Irpinia and L'Aquila in Italy. The described methodology includes an explanation of seismic vulnerability in terms of fragility curves and the classification of vulnerability for exposed buildings, based on the building attributes from the national census. Analytical methods are applied to both individual structures representing categories of buildings and sets of randomly generated buildings. These models are created through structural analysis techniques and are subjected to specific lateral load patterns or accelerograms [6]. Among the analytical approaches for assessing seismic vulnerability of masonry structures, the following stand out [15]: FaMIVE (Failure Mechanism Identification and Vulnerability Evaluation), which defines fragility functions and conducts vulnerability assessments [16; 17]; SP-BELA (Simplified-Pushover-Based Earthquake Loss Assessment), which is focused on vulnerability functions [18]; CSBM (Capacity-Spectrum-Based Methods), which obtains fragility functions and addresses structural and economic damage [19]; SELENA (SEimic Loss EstimatioN using a logic tree Approach), which determines structural and economic damage as well as the number of casualties [20]; HAZUS (HAZard U.S), which determines fragility functions and evaluates damage from both structural and economic perspectives [21]; and RMTK (Risk Modellers' Toolkit), which is focused on fragility and vulnerability functions, as well as seismic risk assessment [22]. META-FORMA-XL [3] is an automated processing tool that was recently used in a case study in Foggia, Puglia, in southern Italy. This methodology was used to estimate seismic fragility curves for masonry aggregates in historic centers by combining data from multiple sources and automated typological-mechanical modeling of archetype buildings.