## 4 Materials and Methods
### Methods  
CNN architectures come in various dimensions, including 1D, 2D, and 3D formats. Among these, 2D CNNs are most prevalent and are commonly employed for tasks such as image classification, similarity clustering, and object recognition in scenes. The rationale behind the prevalent use of 2D CNNs in image classification stems from the inherently \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Damage Case** & **Bolt 1** & **Bolt 2** & **Bolt 3** \\ \hline
2 & 0 & 1 & 0 \\
3 & 0 & 0 & 1 \\
4 & 0 & 1 & 1 \\
5 & 1 & 0 & 0 \\
6 & 1 & 0 & 1 \\
7 & 1 & 1 & 0 \\
8 & 1 & 1 & 1 \\ \hline \hline \end{tabular} Here, “1” represents the system being damaged, while “0” shows that the system is healthy.  
\end{table} 
Table 1: Damage cases with the states of the bolts. two-dimensional nature of image data [32]. Conversely, Conv1D architectures are typically applied for analyzing time series data. Given that the vibration data obtained from building structures also exhibit temporal characteristics, Conv1D architectures can be effectively utilized for the classification of acceleration data [33]. In the context of this research, a convolutional neural network (CNN) model was developed. Although CNNs operate as black box systems, where input batches are processed to yield corresponding outputs, designing effective machine learning models involves selecting appropriate algorithms and techniques, which in turn require decisions regarding specific parameters [34]. In deep neural network models, designers must determine key factors such as dropout rates, the number of layers, and the quantity of neurons. However, deciding on these parameters is often not a straightforward process, as their optimal values may not be immediately apparent. These parameters, which vary depending on the problem and dataset, are known as hyperparameters. Different combinations of hyperparameters may yield varying levels of model performance, and selecting the most suitable combination is a crucial challenge. Typically, hyperparameter selection relies on the designer's intuition, past experience, reflection on applications in related fields, current trends, and the inherent design characteristics of the model. However, recent advancements have introduced techniques aimed at systematically identifying the most appropriate hyperparameter combinations for optimal problem solving. The number of hyperparameters in a model can vary significantly, ranging from just a few to several hundred. Examples of hyperparameters include the number of layers and epochs, kernel size, stride, padding batch size, activation function, layer types, and units. Hyperparameter tuning is essential for creating the most effective model for a given dataset, and various methods exist for achieving this goal. After discussing the basics of convolutional neural networks (CNNs) and their application in vibration-based structural damage detection, it is important to delve into the concept of hyperparameters and their significance in model design and performance optimization. In machine learning models, hyperparameters are parameters whose values are set before the learning process begins. These parameters govern the behavior of the model during training and influence its ability to learn from the data. Some common hyperparameters in CNNs include the following:  
**Number of Layers:** This refers to the depth of the neural network, including convolutional layers, pooling layers, and fully connected layers. Deeper networks can potentially capture more complex patterns but may also be prone to overfitting. **Epochs:** An epoch is one complete pass through the entire training dataset. The number of epochs determines how many times the model will see the entire dataset during training. **Kernel Size:** In convolutional layers, the kernel size defines the spatial dimensions of the filter applied to the input data. Larger kernels capture broader patterns, while smaller kernels focus on finer details.