# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 6 Conclusions  
Earthquakes threaten many lives every year. They affect not only developing countries but also developed ones and they cause significant economic losses, massive deaths, as well as the suspension of occupancy and businesses. For future losses to be minimized, it is crucial to estimate seismic damage before an earthquake occurs. Existing buildings in earthquake-prone areas need structural safety or seismic vulnerability assessments. Various RVS techniques have been developed by different countries in recent years to deal with the devastating effects of earthquakes on buildings and humans. However, RVS methods have some limitations, such as surveyor bias and site-specific characteristics, as well as data uncertainty and vagueness. Fuzzy logic, neural networks, and ML are all computer algorithms that can be used to overcome these kinds of disadvantages in evaluation. Turkiye is located in an active earthquake zone that can cause very severe earthquakes; every two years, it experiences severe earthquakes, and every three years, it experiences very severe earthquakes. Therefore, a new model was developed to rapidly assess building seismic performance. A consensus-based Likert-ANN approach has been used in this study to predict earthquake damage levels in residential buildings with RC frames. The ANN was designed by analyzing the visual images of buildings damaged by the devastating Kahramanmaras earthquake of 6 February 2023, which measured 7.7 and 7.6 on the Richterscale and severely affected 11 districts in Turkiye. Vulnerability variables for buildings are measured using linguistic data on a five-point Likert scale based on expert consensus values derived from post-earthquake visual images. An extensive literature review was conducted to determine the building vulnerability parameters required for the proposed model, including the top hill-slope effect, weak story effect, soft story effect, short column effect, plan irregularity, bounding effect, heavy overhang effect, number of stories, construction year, structural system state, and apparent building quality. Structural analyses categorized buildings as no damage, slight damage, moderate damage, or severe damage/collapse. In terms of damage levels, 48 of the buildings had no damage, 49 had slight damage, 48 had moderate damage, and 49 had severe damage or collapse. Model inputs consist of risk indices ranging from 0 to 1, while outputs consist of damage levels varying between 0.25, 0.50, 0.75, and 1. A two-layer neural network (11-11-1) with LMBP was used to train the model after 194 buildings up to seven stories were evaluated. Max_fail (five) was reached in the proposed model's 794 th iteration, and training was completed with an adequate performance value (\(mse=7.26306\times 10^{-5}\)). Statistically, errors in the entire data set (e = t - a) are \(M=0.00068\pm STD=0.00852\), with 0.02 on one data point, 0.06 on one data point, and 0.07 on two data points. Using this model, it is possible to assess the seismic vulnerabilities of existing buildings by analyzing pre-earthquake facade images with 11 parameters. Experts can use linguistic variables on a five-point Likert scale, and consensus values combine differing opinions from experts. LMBP provides a high-performance solution to the proposed model. Although the model shows good performance and it can be used for rapid visual analysis, a detailed analysis may still be needed. There is room for improvement. A further evaluation of the model's reliability should be performed with multiple expert groups and with data from a variety of earthquake zones or should be remodeled with more data. This requires much time, but if an AI application evaluates relevant parameters from photographs, this time will be much shorter, and the proposed model will become more useful. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.