# A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings
## 1 Introduction
A substantial literature has been published in the attempts of integrating several methods from various domains with RVS, for instance, statistical methods [11; 12], Artificial Neural Network (ANN) [13; 14; 15; 16; 17], multi-criteria decision making [18; 19], and type-1 [20; 21; 22; 23] and type-2 [24; 25] fuzzy logic systems are frequently assimilated within RVS for increasing the interface and efficacy of seismic vulnerability screening. However, there are methods developed to evaluate the damage and change detection of buildings by remote sensing and image analysis [26; 27]. The parameter significance and the selection of the optimum number of parameters have been used effectively by [PERSON] and [PERSON] [28]. Multiple linear regression analysis [11] is the most commonly used statistical technique for the classification of damage state in the RVS domain, preceded by other approaches, such as discriminant analysis suggested in [29]. [PERSON] and [PERSON] [15] have effectively applied and utilized eight different statistical risk classification methods, including six-building output modifications (predictor variables) with damage states as none (O), light (L), moderate (M), severe (S), and collapse (C). Furthermore, [PERSON] et al. have proved the integrated use of various exciting variable selection techniques such as R\({}^{2}\) adjusted, forward selection, backward elimination, and [PERSON]'s and Bayesianinformation criteria [30]. Besides, the traditional least square regression analysis and multivariable linear regression analysis were used in the research proposed in [31]. Several probabilistic models are suggested, such as reliability-based models and \"best estimate\" matrices of the potential damage [32]. [PERSON] and [PERSON] [33] have proposed a unique and innovative ANN application from a practical perspective. The coupling of fuzzy logic and ANN's has been used in a study by [PERSON] [34], where untrained fuzzy logic procedures are remarkable. Nevertheless, several of the previous RVS approaches are based on the expert's view, complexities, or conclusions in the linear parameter relationship. Machine Learning (ML), an artificial intelligence division, uses computational algorithms capable of automatic creation by training and learning. This focuses on rendering the forecasts using data analysis software. The ML algorithms generally comprise of three fundamental components: representation, evaluation, and optimization. A method known as Support Vector Machine (SVM) has been commonly used in scenarios of processing uncertain data or data without necessary records that need to be processed efficiently. SVM performs adequately even for the provided information in the context of unstructured and semi-structured data, such as tests of images merged with trees. Kernel trick is one of SVM's strengths that integrates all the knowledge required for the learning algorithm to identify a core object in the transformed field [35]. The implementation of SVM with initially supervised learning data-sets is seen in this analysis. The damage records of 4 different earthquakes have been taken to implement from countries such as Ecuador, Haiti, Nepal, and South Korea. Finally, the work demonstrates the SVM model's expertise and the detailed interpretation of the chosen earthquakes result.