## 1 Introduction  
The study of the dynamic response of structures under ambient vibrations is fundamental in many engineering fields, including, but not limited to, Structural Health Monitoring (SHM). Even in the range of small linear deformations, such as are observed under ambient excitation, understanding the dynamic behavior of a system might be challenging, especially when testing rigid and massive structures. To make things more difficult, there are then the interactions with the surrounding environment, the uncertainty in geometry, materials characteristics, details, and above all the difficulty in defining the constraints, which often call for simplified models to drive the modal identification process. In its broadest sense, system identification can be defined as the field of study where models are fitted into measured data [1]. In civil engineering, output-only modal identification techniques allow to significantly extend the range of structures where modal analysis can be applied [2], overcoming the difficulty deriving from producing and measuring proper excitations in large-sized structures. Practically, ambient vibration testing is used in all contexts in which only the dynamic response can be measured, while excitation (e.g., wind, traffic, environmental noise, etc.) is known only in a probabilistic sense or is even unknown [3, 4]. Like in any other kind of experimental modal analysis, the measured data come from the record of the sensors at different locations of the structure [3]. A comprehensive amount of literature on the comparison of output-only modal techniques can be found in [4, 5, 6, 7, 8]. Throughout the years, output-only dynamic identification relied primarily on the time-domain approach, which declines in many robust and accurate algorithms [7]. Since the theoretical part overcomes the goal of this paper, references can be found in [9; 10; 11; 12; 13]. Time-domain techniques, in particular, are demonstrated to be very effective in the detection of closely spaced modes, easy to optimize, and automate [13]. It should be also pointed out that, in the presence of strong non-stationary components, a possible option is recurring in time-frequency representations and algorithms [14]. The main results deriving from linear identification techniques are the modal parameters of the structure, as they result from diagrams of stabilization to the varying of the order of the system used in the identification. The discrimination of authentic modal components from spurious ones is achieved with the use of modal assurance criteria, and sometimes exploiting clustering techniques, which consist in dividing different data from a data set into property-based groups. However, the detection and classification of the authentic modal parameters from the numerical solutions to the inverse problem are not exempt from criticalities. The main critical aspect certainly lies in the well-known limitations of experimental modal analysis procedures in massive or otherwise rigid structures. Indeed, identification algorithms have been successfully applied to structures presenting a diaphragmatic behavior, for instance on multi-span concrete bridges, e.g., see [10; 13; 15; 16]. However, the sensitivity of the identification process to the external or mutual constraints of these diaphragms has never been investigated. The second criticality concerns the choice of the model used in the identification process. In structures with complex, sometimes non-linear, interactions, a choice could be to adopt black-box models [17; 18]. In spite of many successful applications of such an approach, the solution of the inverse problem strongly depends on the choice of the parameters of the black-box model [19]. Thus, an alternative approach consists of the improvement of analytical models using test data [20], possibly recurring to surrogate models to increase the computational efficiency of the whole process [21].