## 1 Introduction  
Dynamic characterization involves assessing the intrinsic properties of a structure, independent of the type or magnitude of the applied loads. It is a non-destructive investigative method suitable for various situations, ranging from newly constructed buildings to existing structures of historical or artistic significance. In the case of new structures, it is typically used to validate assumptions made during the design phase, such as material properties and structural stiffness. Applied to older or historically significant buildings, this method predominantly captures the actual structural behavior, helping in planning necessary safety interventions [1, 2, 3, 4]. Numerous studies reported in the literature focus on the dynamic analysis of structures using this investigative method [1, 2, 3, 4, 5]. This approach also facilitates the identification of potential local or global damages within the structure, which, depending on their severity, can lead to a decline in performance. Typically, this deterioration is observed as a stiffness decrease, resulting in changes in frequencies and vibration modes, ultimately affecting the structure's dynamic response [5, 6]. Another application of experimental dynamic characterization concerns the study of dynamic loads acting during the service life of the structure. These loads could cause significant deformations that must be considered when analyzing structural responses and performing structural inspections. Examples include large-span bridges, dams, and towers subjected to wind action, seismic activity, vehicular or railway traffic, vibrations from heavy industrial machinery, or explosions in specific contexts (e.g., quarries) [7]. Dynamic characterization has gained widespread use due to an extensive architectural heritage and increasingly complex structures. Concurrently, significant technological progress in computing and electronics has enabled the study of static and dynamic properties of structures and comparisons between numerical models and actual structural behavior. The integration of experimental data and probabilistic methods has been suggested to address the uncertainties inherent in material and geometric variability. From such comparisons, substantial differences have often emerged between the mechanical properties derived from numerical modeling and those of the actual structure. These differences may arise from incomplete information about the structural material properties. Specifically, the natural variability in material characteristics, stemming from aging, degradation, and limited historical documentation, leads to deviations between model predictions and real-world behavior. Material properties and geometry are the origin of mass, stiffness, and damping properties, making it complicated to reproduce reality in the model [8]. Furthermore, the extent of these differences depends on the type of modeling conducted (finite element modeling, discrete models, macro-models, etc.) [9]. Recognizing these uncertainties is vital for enhancing safety, refining retrofit strategies, and ensuring more reliable seismic assessments of historical buildings. Experimental techniques are necessary to bridge the gap between the real structure and the numerical model. The goal is to calibrate the model using experimental results that better represent the structure's dynamic properties. Dynamic identification techniques are of two types, input-output techniques in which the structural response (output) is measured through excitation at one or more points (input), and output-only techniques based on measuring the structural response (output) excited by environmental sources (e.g., vehicular traffic and wind), assuming that the vibration-generating entity is a stochastic process. Input-output techniques, known as Experimental Modal Analysis (EMA), allow for estimating modal parameters, including modal frequencies, damping ratios, mode shapes, and modal participation factors.