## 2 Literature Review  
Vibration-based building health monitoring systems, which have been developing recently, serve as an example of non-destructive testing systems. In vibration-based structural health monitoring or building damage detection, researchers focus on studies aimed at determining the location, time, and severity of damage in existing structures [1; 2; 3; 4; 5]. Many techniques have been developed to obtain information about structural health by examining the vibration responses of a structure [6; 7; 8; 9; 10]. In such techniques, the response of the building to forced or free vibrations by means of accelerometers placed at certain points of the building is tested to obtain information about the structural health by using different algorithms [11; 10; 12]. We can categorize vibration-based structural health monitoring methods into parametric and non-parametric approaches. In parametric vibration-based structural health monitoring, the dynamic parameters of the building (such as modal frequency, mass, rigidity, and mode shapes) are computed from the acceleration data collected by accelerometers [13; 14; 15; 16; 17]. In the parametric method, structural damage estimation is conducted by comparing the dynamic parameters of the damaged building with those of the undamaged building [19; 20]. In non-parametric vibration-based structural health monitoring, on the other hand, an attempt to estimate structural damages is carried out by directly processing acceleration data. In certain studies, researchers have combined time series analysis with statistical classification to uncover building features that exhibit distinct responses in the event of damage from raw signals. Subsequently, these signals were monitored using a classification tool to assess the health status of the building [21; 22]. With the advancement of machine learning algorithms, a subset of artificial intelligence, and the evolution of computer components capable of processing large datasets, machine learning has gained significant importance in various aspects of our lives. It has begun to streamline human activities across a wide spectrum, from gaming to military applications and from auto-completing search engine queries to enabling driverless vehicles. Artificial intelligence continues to perform tasks that either take humans a longer time to accomplish or are beyond human capability entirely. However, since the developers of artificial intelligence algorithms are currently human, it falls upon humans to identify the most suitable artificial intelligence model and hyperparameters for a given task. This process is time-consuming, particularly in the development of deep learning algorithms, and requires extensive research and expertise to make informed decisions [23]. Machine learning approaches can be categorized into three main types: supervised, unsupervised, and reinforcement learning. Supervised learning is a machine learning paradigm that involves mapping an input to an output based on sample input-output pairs [24]. In simpler terms, supervised learning can be described as teaching the algorithm using a dataset with labeled examples and then requesting the system to provide possible outputs for new inputs. On the other hand, unsupervised learning involves extracting patterns or structures from unlabeled data without any predefined categories or labels [25]. In other words, unsupervised learning entails classifying data based on their inherent similarities or differences without relying on predefined categories or labels for unclassified or unlabeled data. Finally, reinforcement learning is a machine learning approach inspired by behaviorism, focusing on determining the actions that subjects should take in order to maximize the rewards within a given environment [26]. In a recent study, researchers proposed a method for developing MISMs on structural systems, utilizing structured input data to enrich mechanical understanding.