## 1 Introduction
### Background of Study
[PERSON] and [PERSON] [34] have explicitly proved that ANN could be practically applied in RVS from an innovative perspective, carried forward by the pioneering application of the coupling of fuzzy logic and ANN's by [PERSON] [35]. In this research, untrained fuzzy logic procedures have shown exceptional performance. Even so, the RVS procedures adopted presently are largely dependent on the evaluator's perspective, errors, and uncertainties or the crude assumption of linear dependency in seismic parameters. ML, a subdivision of artificial intelligence, allows highly efficient computing algorithms with training and learning for instinctive development. It aims to make forecasts using data analysis software. The ML algorithms typically include three necessary components: description, estimation, and optimization. The domain of the study contained multi-class classification problems that require multi-class classification techniques of ML. [PERSON] and [PERSON] [19] conducted a study using eight different statistical techniques, such as K-Nearest Neighbours, [PERSON]'s linear discriminant analysis, partial least squares discriminant analysis, neural networks, Random Forest (RF), Support Vector Machine (SVM), Naive Bayes, and classification tree, to assess the seismic risk of the reinforced buildings while using six feature parameters. Extra Tree (ET) and RF performed better than the others, and the authors suggested further investigation and calibration of the result using more feature vectors and big datasets. The study conducted by [PERSON] [36] has emphasized various machine learning applications for building structural design and performance assessment (SDPA) by reviewing Linear Regression, Kernel Regression, Tree-Based Algorithms, Logical Regression, Support Vector Machine, K-Nearest Neighbors, and Neural Network. The previous achievements in the field of SDPA are categorized and reviewed into four sections: (1) anticipation of structural performance and response, (2) clarifying the experimental data and creating models to forecast the component-level structural properties, (3) data recovery from texts and images, and (4) pattern identification in data from structural health monitoring. The authors have also addressed the challenges in practicing ML in building SDPA. In another study performed by [PERSON] et al. [37], SVM performed comparatively efficient even in the case of many features. Kernel trick is one of SVM's strengths that blends all the knowledge required for the learning algorithm, specifying a key component in the transformed field [38]. This work's focus includes demonstrating and utilizing a multi-class classification of ML techniques for preliminary supervised learning datasets. Quality and quantity of data for supervised machine learning models play a significant role in achieving a high yield working model. Focusing on this, accurate post event data are collected from four separate earthquakes, including Ecuador, Haiti, Nepal, and South Korea. The issue of data shortage is crucial as data are the building blocks of ML models. Out of several supervised learning models, the authors have selected five influential algorithms: SVM, K-Nearest Neighbor (KNN), Bagging, RF, and Extra Tree Ensemble (ETE). Finally, the research demonstrates the feasibility of the proposed ETE model and the detailed analysis of the earthquake results.