# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 1 Introduction  
Many lives are threatened every year by earthquakes. It is not only developing countries that suffer from the negative effects of earthquakes on the economy [1] but also developed ones that experience significant economic losses, massive fatalities, and the suspension of occupancy as well as businesses every year [2]. A structural safety assessment or conducting a seismic vulnerability evaluation of existing buildings is crucial, since some structures may be susceptible to potential risks [3] in earthquake-prone areas, including some of them which might have been constructed before seismic codes were developed, which may mean they were not designed to resist earthquakes and therefore may not be strong enough to withstand them, and the condition of a building may also have declined as a result of a change in use or land with high liquefaction potential, or as a result of wear and tear over time [1]. Moreover, city planning and construction quality are problematic in developing countries due to insufficient expertise, imprecise legislation, insufficient funds, and unplanned urbanization, which results in low-quality materials andwork causing earthquake deaths [4]. Estimating seismic damage before an earthquake is crucial to reducing future losses [5], and assessing building damage after an earthquake is an essential part of emergency response and recovery efforts as well. The level of building damage can range from superficial cracks to complete collapse depending on the building's properties, soil conditions, and earthquake and ground motion characteristics [6]. Based on the results of a seismic evaluation, a decision will be made as to whether the building needs to be repaired or renovated to increase its resistance to seismic forces, or if it should be demolished [7]. In recent years, alternative methods have been developed to rapidly identify deficient structures from large building stocks [8] and various rapid visual screening (RVS) techniques have been developed by different countries to deal with the devastating effects of earthquakes on buildings and humans [9]. RVS techniques all have their specific characteristics, but basic structural characteristics like irregularities are taken into consideration; however, the final scores of building vulnerability classifications differ significantly due to differences in conventional RVS techniques [10]. [PERSON] and [PERSON] argue that RVS assessments can be used to evaluate the vulnerability of buildings in different regions, with appropriate adaptations [3]. It is therefore possible to calibrate existing RVS methods or to develop new RVS methods to overcome the deficiencies [3]. There are some limitations to traditional RVS methods, including surveyor bias, site-specific characteristics, and the uncertainty and vagueness of the data. Several computer algorithms can be used to overcome these kinds of disadvantages in evaluation, such as fuzzy logic, neural networks, and machine learning. It is generally agreed that further research is needed to implement or modify the method, according to [PERSON] et al. [11] and [PERSON] et al. [12], and that the methodology needs to be developed based on pre- or post-earthquake data, detailed seismic risk assessment methodologies, and/or soft RVS methods. With continuous innovation and refinement of the methodologies, the quality of the results is greatly improved [13]. Turkive is located in an active earthquake zone that can cause very severe earthquakes. Turkive experiences a severe earthquake every two years, while a very severe earthquake happens every three years, according to a statistical study [14]. Moreover, [PERSON] et al. came to the conclusion that almost all of Kahramanmaras, Turkey's seriously damaged or fallen reinforced concrete structures were built between 1975 and 2000, when site inspections were rare or nonexistent, and the buildings were not designed and constructed according to the seismic codes that were in effect at the time [15].