## 1 Introduction
### Background of Study  
The RVS approach has been used to determine the structural damage states and index of the affected buildings [6,7]. The FEMA in the United States identified the demand for a rapid, reliable, and non-rigorous method for damage state classification in 1988 [1]. The fundamental method (1988) was later subjected to revisions in 2002 to incorporate the latest research advancements in seismic engineering. A highly relevant and adequate explanation of this approach has been presented in the research by [PERSON] and [PERSON] [8]. This approach was further adopted by various countries across the globe for damage state characterization [2] with adequate customization in the method according to country-specific materials, methods, and design philosophies in construction--for example, Indian RVS approach (IITK-GSDMA) [9]. RVS is highly effective in analyzing a large building stock due to rapid computational time and non-rigorous methodology. Seismic vulnerability assessment (SVA) is performed mainly in three stages [10]: walk-down evaluation, preliminary evaluation, and detailed evaluation. The SVA approach's primary component begins with the physical presence of the trained evaluator with the respective data sheets onsite by performing a walk-down survey to record the seismic parameters of the structure. In many instances, the evaluator needs to enter and have access to the building; architectural moldings hide the structural members for aesthetic purposes. The structures that struggle to fulfill the expectations shall be subjected to the second stage. The second stage comprises a more detailed and extensive analysis in which seismic aspects to different structural characteristics such as the actual ground conditions, the consistency of the material, the condition of building elements, etc. shall be considered. If found to be necessary, the structure shall be subjected to the final stage, where a more detailed and rigorous analysis is used. In this stage, an in-depth and profound nonlinear dynamic analysis and research of the building's seismic response shall be studied, which shall also help to optimize the application of seismic strengthening techniques on the structure [11]. The RVS is a scoring-based approach, wherein performing some elementary computations result in the final performance score for predicting the damage state classification of that structure. Every RVS approach from different countries has its predefined cutoff scores. Extensive research has been published to integrate scientific methods from several domains to blend them into the scope of RVS and propose a smarter resultant approach; for instance, statistical methods [12; 13], ANN and ML techniques [14; 15; 16; 17; 18; 19; 20], multi-criteria decision-making [5; 21], deep learning classification [22], and type-1 [23; 24; 25; 26] and type-2 [27; 28] fuzzy logic systems. Within the framework of statistical methodology and regression analysis, multiple linear regression analysis [12] is the most commonly used technique for damage state classification in the RVS domain [2], preceded by other approaches such as discriminant analysis proposed in [29]. [PERSON] and [PERSON] [30] have investigated the application of seismic parameter selection algorithms by choosing the optimum number of seismic parameters that can upgrade the performance of the RVS. Furthermore, [PERSON] et al. have proved the integrated use of various interesting variable selection techniques such as R\({}^{2}\) adjusted, forward selection, backward elimination, and Akaike's and Bayesian information criteria [31]. Furthermore, in the research proposed in [32], the traditional least square regression analysis and multivariate linear regression analysis were used. [PERSON] and [PERSON] [33] suggested several probabilistic models, such as models based on reliability and \"best estimate\" matrices of the likelihood of damage for a different seismic zone by combining expert opinion and the damage statistics of past earthquakes.