# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 2 Related Studies  
Various seismic risk assessment approaches may be used to evaluate the structural integrity of existing structures [18], but they require complex analysis tools, detailed geometric and material data, as well as considerable time and expertise [4; 8]. It is not feasible to use these detailed procedures to assess a large number of buildings at once due to a lack of time, resources, and manpower [4]. RVS is used to identify buildings most vulnerable to earthquake-induced damage by classifying them according to the seismic risk level and prioritizing them accordingly [19]. RVS procedures are divided into three main categories: national methods, statistics and machine learning methods, and rule-based methods [20]. These methods have been used in many studies. The following is a summary of a few studies. Various performance modifiers in the national methods including FEMA P-154, the EMS-98 scale, the RISK-UE Project, NRCC, RBTE-2019, etc., are used to quantify the vulnerability of buildings, such as building type, vertical irregularities, plan irregularities, construction quality, and year of construction [20]. The performance modifiers can be collected easily through observations and engineering drawings [21]. A statistical regression method was employed by [PERSON] and [PERSON] to analyze the relationship between the outcome variable (the damage grade of existing buildings) and the explanatory variables regarding building attributes that increase the vulnerability of buildings to earthquakes. For calculating the seismic performance scores of buildings, [PERSON] et al. proposed a general linear equation, and the weights of each vulnerability parameter were determined statistically [22]. In addition, predictor variables and damage grades were modeled as linear relationships [22]. The use of soft computing techniques in seismic assessments of existing buildings reduces human effort dramatically and improves accuracy [20]. In recent years, several soft rapid visual screening (S-RVS) approaches have been created. These methods can be quickly modified based on breakthroughs and prior earthquake data, and they provide more accuracy compared to traditional RVS methods [18]. S-RVS methods utilize artificial intelligence (AI) such as machine learning (ML) [9; 17; 23], Fuzzy Logic (FL) [23; 24; 25; 26; 27], and Artificial Neural Networks (ANNs) [28; 29; 30; 31] in their development process. In recent years, AI has received significant attention in post-earthquake inspections because of its rapid advancement and potential to overcome the disadvantages of manual inspections [31] and has been extensively used in various scientific and technical fields. Studies on the use of these methods in civil engineering have shown promising outcomes and decreased reliance on human involvement, hence minimizing uncertainty and bias [9]. A study published by [PERSON] et al. [6] examined the effectiveness of four ML algorithms in predicting earthquake damage to buildings, including discriminant analysis, K-nearest neighbors, decision trees, and random forests. In the study of [PERSON] et al., a comprehensive overview of different AI techniques for analyzing existing buildings and evaluating damage is provided [17]. [PERSON] et al. developed a web-based application to classify earthquake damage that can automatically and effectively categorize structural damage [32]. By using gradient-weighted class activation mappings and the convolutional neural network (CNN) model, the degree of damage was assessed [31]. FL evaluates linguistic and information uncertainties and is more realistic when it comes to qualitative assessment than crisp logic [26]. [PERSON] proposed a supervised FL classification approach for assigning hazard categories to buildings similar to the classical fuzzy C-means approach [25]. A fuzzy inference system was proposed by [PERSON] et al. to determine the building damage hazard category following earthquakes based on a fuzzy inference system.