## 1 Introduction
Such tension is transmitted from the BIKD to the superstructure through a connecting cable, allowing the isolator to resist relatively high levels of traction compared with the requesting axial load. In addition, it provides friction energy dissipation in the ball joint that serves as basal support [13; 14]. Computational models for dynamic systems of structures can be highly expensive to run. This cost is increased when there are many parameters involved in the system, when the structure has many degrees of freedom, and/or when there is non-linearity involved in the system. Therefore, it is important to identify critical parameters of dynamic system models since it would simplify models, in order to simplify the model and reduce computational costs [15]. Sensitivity analysis (SA) determines how the variability of a given model's outputs can be explained as a function of variations in its inputs. By studying this relationship, it is possible to identify factors or variables of greater and lesser importance on the results of interest of the model. SAs are classified into two groups: local sensitivity analysis (LSA) and global sensitivity analysis (GSA) [16]. The local sensitivity analysis considers the variability of the outputs versus the variation of one input parameter around a specific value, whereas the global sensitivity analysis considers the complete space of variability of the input factors. Another difference between them is that the LSA modifies one parameter at a time while the others remain unchanged, whereas the GSA allows the variation of all the parameters simultaneously to evaluate their influence on the outputs of a model [17]. One of the GSA methods widely used in structural engineering is a variance-based sensitivity method [18; 19; 20; 21]. This method analyzes the variance of the output using Monte Carlo Sampling (MCS) or Latin Hypercube Sampling (LHS) techniques. Various investigations and applications can be found on this type of analysis to determine the sensitivity of the parameters in dynamic structural systems. [PERSON] et al. [18] proposed an efficient approach based on random sampling to estimate the upper limits of the total sensitivity indices. In this study, it was found that the number of evaluations required in the model was reduced using MCS. They applied this approach by analyzing the parameters that affect the variance of the maximum displacement of a specific point in a mechanical model of a long bridge, applying a harmonic load as input. [PERSON] & [PERSON] [20] used variance-based sensitivity analysis to determine the sensitivity of the evaluation criteria in selecting the design parameters of a system. Through this analysis, the authors determined the optimal configuration of the system in terms of its design parameters. However, this method has limitations, one of which is that the generation of the output sample is very computationally demanding. This is a major limitation for the applicability of variance-based methods, especially since obtaining accurate estimates can require a large number of output samples. Another limitation of variance-based methods is that they implicitly assume that variance is a sensible measure for measuring uncertainty, which may not always be the case [16; 22; 23]. Due to the limitations of variance-based methods, a series of studies on density-based methods has recently been conducted. These density-based methods have been so named because they investigate the probability density function of the model output, rather than just its variance. One of the density-based GSA methods is the PAWN method [10; 11; 12; 13; 14; 15; 16]. This has been used successfully in various investigations focused on environmental and hydrological models [24; 25]; however, its use was not found in the area of structural or earthquake-resistant engineering. Taking the latter into account, the application of the PAWN method in this engineering area is interesting and novel, since it has advantages over the GSA methods that have previously been used in this area.