# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 5 Discussion  
[PERSON] et al. developed a framework using ML for the analysis of the seismic vulnerability of existing buildings, which identifies buildings in an area and uses their photos to generate a vulnerability index, thereby reducing the time and effort required for surveys and interviews [13]. Similarly, a proposed model was developed based on the structural analysis of post-earthquake facade photographs in the Kahramanmaras earthquake zone, and its seismic performance was evaluated. A five-point Likert scale with consensus values derived from linguistic data on the visual images was used to measure building vulnerability levels. Several predictor variables were used as input parameters for the ML algorithms, including the closest distance to the surface projection of fault rupture and the spectral acceleration at the site of interest. Variables such as building age  
Figure 6: Model’s neural network performance graph.  
Figure 7: Target–output graph after training.  
(in years), number of stories, plan irregularities, floor area, and replacement cost were also used to predict building performance. Green, Yellow, and Red were the damage tagging categories [6]. In another study, [PERSON] et al. scaled component damage levels and global damage levels according to no damage, slight damage, moderate damage, severe damage, and collapse. Similarly, our study defined eleven input vulnerability parameters with five membership values each. Based on the structural analysis, buildings were classified as having no damage, minor damage, moderate damage, or severe damage/collapse.  
Various AI techniques have been used in the literature to design models, and their performances have been analyzed. [PERSON] et al. presented a method in which a building's performance level was categorized as low risk, medium risk, or high risk, claiming that the method showed 83% accuracy, compared to the long and hectic code-based method [47]. An approach based on FL with a genetic algorithm was used by [PERSON] et al. to assess the damage to buildings with different lateral load-resisting components post-earthquake, and approximately 90% accuracy was achieved in their results [63]. [PERSON] et al. examined the effectiveness of ML applications in damage prediction using a support vector machine model as the damage classification technique [33]. According to the study, all 22 input parameters resulted in a 52% accuracy rate, which is acceptable given the sample size [64]. According to the results of the study by [PERSON] et al., predictions made using the random forest algorithm had a 66% accuracy in assigning tags and 79% accuracy in actual yellow tags [6]. In the study by [PERSON] et al., a generalized regression neural network and an FFBP neural network were used for the network architecture [44]. The nonlinear incremental mode combination method was used to evaluate the earthquake performance of these building models. They claimed that an ANN could successfully predict the earthquake behavior of reinforced concrete buildings [65]. [PERSON] and [PERSON] developed a learning model that enables early disaster risk reduction decisions by automating building vulnerability assessments in seismic areas [54]. The proposed model here, however, uses an ANN with two layers (11-11-1) and LMBP to predict the seismic performances of existing buildings. The structural integrity of the buildings was assessed using a Likert scale, and four levels of damage were differentiated. In the following step, input data from the building properties and output data from the structural analysis results were combined to create the proposed model. Training the model resulted in quite good performance. Based on the statistical analysis of the entire data set, the mean and the standard deviation of the errors were 0.00068 and 0.00852, respectively. Four data points differed from the others but were quite acceptable: 0.02 on one data point, 0.06 on one data point, and 0.07 on two data points.