## 2 Review of Rapid Visual Screening Procedures  
Federal Emergency Management Agency (FEMA), USA in 1988 has proposed the initial Rapid Visual Screening (RVS) methodology as \"Rapid Visual Screening of Buildings for Potential Seismic Hazards: A Handbook\" [6]. Furthermore, in 2002, because of earthquake disasters in the 1990s, the methodology was modified to integrate the latest technological advancements [7]. RVS has been widely used in seismic countries as a practical and simple tool for evaluating the vulnerability of buildings; therefore, this challenge is in the interest of many researchers and still is under development and improvement. In this manner, some efforts have been made to develop national RVS for India (IITK-GSDMA) [8], Turkey (EMPI) [9], Italy [10], and the Philippines [11]. Some practical comparisons carried out between different national RVS methods and their robustness have been discovered [12; 13; 14]. Although RVS is an acceptable estimation method that gives a general overview to do proper earthquake mitigation planning, the variables considered contain a level of uncertainty [15].  
In addition to national and local RVS methods, there are many other RVS methods developed by using linear regression [16; 17], Multi-criteria decision making [3], Artificial Neural Networks (ANNs) [18; 19; 20; 21], Fuzzy Logic (see Table 1), and some other methods concerning their consideration and experiences on a region or country scale [22]. These methods can be categorized into two groups: (a) methods based on statistical and machine learning approaches, like linear regression and ANN, and (b) methods based on expert systems such as fuzzy-based methods.  
Methods based on statistical approaches reduce the problem to a linear relationship between inputs and the output, which is not realistic as the relation between building parameters, seismicity parameters, and damageability is a non-linear relation. Methods based on machine learning approaches suffer from a lack of data where there is not enough datasets consisting of building parameters before earthquakes and type of damage after earthquakes. In addition, these approaches are local and limited to a specific area; last approaches, based on fuzzy systems, consider the expert's opinion, and model vagueness exist in words that describe the building parameters. Table 1 presents a summary of the fuzzy systems applied for RVS purposes.  
Fuzzy systems contributed significant achievements in vulnerability assessment because of making definite decisions based on imprecise or ambiguous data [23]. The main problem with all previous studies based on conventional type-1 fuzzy logic system (T1 FLS) is that they only consider vagueness in membership functions while does not including all types of uncertainties due to its crisp membership functions. However, this issue can be solved by implementing IT2 FLS as their membership functions (MFs) are themselves fuzzy [5]. Therefore, an RVS method based on an IT2 FLS is considered to overcome the weakness of RVS methods based on the type-1 fuzzy system.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Author(s)** & **Year** & **Studied Data** & **Purpose** & **Parameters** & **Fuzzy Inference** \\ \hline [PERSON] et al. [15] & 2019 & Chiang Rai, Thailand & Earthquake risk evaluation of buildings by using Fuzzy risk model & Building occupancy (occupancy risk index), building vulnerability (FEMA 154 final score), Seismic hazard (PGA) & Hierarchical fuzzy rule-based \\ \hline [PERSON] et al. [24] & 2017 & 1450 (1000 modeling, 400 tests, 50 outlined) & A three-stage fuzzy rule-based model to determine the hazard rate of building on the impact of earthquake for non-engineered houses & Structural (ring balk, floor block, column, foundation), non-structural (wall crack, wall cover, floor