###### Abstract
An innovative method based on fragility analysis has recently been presented in the literature to estimate the separation distance [15]. Different limit states were considered for the inelastic flexural and shear demands, as well as for the inelastic displacement demands of the structure. It is herein stressed that the choice of an EDP is commonly based on either the field observations of the structural failure modes during past earthquakes or the deterministic results of extensive nonlinear research analyses of engineering structures. The overall structural performance is usually estimated by adopting displacement-based EDPs, while local inelastic demand parameters are adopted to monitor the seismic response of individual structural components. Typically, the fragility analysis is based on the performance of the most critical component, while the assessment represents the vulnerability of the whole structure. In this way, the effect of different demand parameters that may be critical for the structural vulnerability is neglected [25]. [PERSON] et al. [25] also highlight the importance of incorporating different EDPs at different limit states when structural fragility analysis is utilized. For this reason, a multidimensional fragility-based method was proposed, and a multivariate PSDM was introduced to study the dependency between the demand parameters. In this direction, multiple failure models of different structural components were also considered to generate accurate explicit connectivity BNs for the structural reliability assessment [26]. Seismic fragility analyses have also been performed by [PERSON] and [PERSON] [27] to evaluate the effect of different rounding configurations on the performance of non-seismically designed RC structures. As far as the authors are aware, in the framework of the probabilistic assessment of the structural bounding effect, other key issues that have been addressed in the literature are the efficiency and sufficiency of the intensity measures (IMs) [13; 20], the adopted methodologies for developing accurate fragility curves [28], and the effect of the magnitude \(\mathrm{M_{w}}\) and \(\mathrm{R_{np}}\)[29]. Nevertheless, [PERSON] et al. [30] introduced a novel probabilistic data modeling method to improve the prediction of demand responses in structural health monitoring measurements. The proposed method is based on an improved sparse Bayesian learning scheme. Beyond all these, risk-based methodologies involving a comparison between the seismic demand and the capacity level to control the acceptable seismic risk have also been presented in the literature [31; 32; 33; 34]. For example, [PERSON] et al. [33] proposed decision models to verify the seismic collapse safety of buildings. For this purpose, a risk-targeted safety factor expressed either by an IM or EDP has been introduced and applied to the capacity of a structure. It is noted that the combination of the seismic hazard of a site with the fragility-based structural analysis provides the demand hazard curves (DHCs) of the structure. In this case, the demand hazard curve expresses the mean annual frequency (MAF) of exceedance of a specified EDP limit state. Nevertheless, limited research studies dealt with the structural bounding risk. [PERSON] and [PERSON] [17] evaluated the bounding risk as the probability of exceeding the code's requiredseparation gap distance during the structure's design lifetime of 50 years. A parametric study was carried out, and the relative displacement demands were evaluated considering different period ratios of adjacent structures. In 2012, [PERSON] et al. [35] introduced a fully probabilistic procedure to assess the structural bounding risk based on the performance-based earthquake engineering (PBEE) concept. The bounding problem was evaluated as the probability of the relative displacement between adjacent buildings exceeding the separation distance.