## 2 Literature Review
They explored graph neural networks (GNNs) as a means of representing and embedding knowledge about structural systems, particularly truss structures. Unlike traditional black box machine learning models, the proposed approach emphasizes the role of structural mechanics in defining the surrogate model, aiming to produce physically based outputs for the problem at hand. Specifically, the researchers developed MISMs to learn deformation maps of the system based on known structural features. Their approach was applied to both bidimensional and tridimensional truss structures, demonstrating superior performance compared with standard surrogate models [27]. In this study, supervised learning algorithms, particularly convolutional neural networks (CNNs), will be utilized. In data-driven machine learning methods, as the name suggests, having a sufficient amount of data is crucial for the algorithm to effectively learn the underlying system and produce accurate outputs. The process of finding or creating a dataset is often the most labor-intensive aspect of supervised machine learning. Deep neural networks (DNNs) refer to cases where artificial neural networks (ANNs) contain more than three layers. Deep learning represents one of the latest advancements in machine learning, with widespread applications across various scientific fields. Deep learning continues to evolve and yield increasingly accurate results. In deep learning methods, particularly CNNs, the networks can autonomously learn to extract features directly from the raw data pertinent to the problem at hand, thereby maximizing classification accuracy. This inherent capability makes CNNs particularly appealing for complex engineering applications [28]. CNNs excel at capturing the spatial and temporal dependencies in signal data through the application of relevant filters. Traditionally, CNNs have been predominantly used in image recognition tasks within the literature. However, in recent studies, CNNs have also begun to be employed in vibration-based structural health monitoring systems, leveraging increased computational power. [PERSON] et al. [29] developed a CNN to ascertain the location and extent of structural damage in a five-story building model. The authors aimed to detect damage in the building model by analyzing the acceleration data from the El Centro dataset. In a similar study, [PERSON] et al. [30] employed a CNN to identify damage in a population of bridge structures constructed using a large number of random models. The damage characteristics of this population were extracted, and the CNN was subsequently utilized to detect damage in newly generated models. The results showed that employing acceleration signals as CNN inputs achieved the highest detection accuracy, reaching 99.4%. This underscores the efficacy of the proposed approach in enabling CNNs to identify damage across multiple structures. This study aims to investigate the performance of a CNN in scenarios where there is an insufficient number of measurement sensors, meaning only one accelerometer is placed on the structure, neglecting the placement of one accelerometer on each floor as is typical. To achieve this, a three-story single-bay laboratory frame is experimentally examined under both sufficient and insufficient sensor configurations. In this context, the effectiveness of the CNN technique is enhanced by applying windowing to the transformed data. The results obtained indicate that the employed CNN approach can successfully estimate damage detection even in situations with limited sensor placement. In order to validate the efficacy of a CNN in scenarios with limited sensor placement, the experimental test set-up serves as a critical component, providing real-world data for analysis and comparison. By meticulously configuring the three-story single-bay laboratory frame under both sufficient and insufficient sensor configurations, the experimental set-up simulates realistic conditions, allowing for a comprehensive evaluation of CNN performance.