## 4 Materials and Methods
### Methods
For example, if we consider hyperparameters like the learning rate, number of layers, and dropout rate, then the search space for each would consist of the various values or ranges within which these parameters could be adjusted. In essence, the search space defines the boundaries within which the optimization algorithm operates to find the optimal configuration for the neural network model. Maintaining a consistent search space across different experiments ensures fair comparisons between models trained on different datasets or with different configurations, allowing for a comprehensive evaluation of their performance. For example, if we were optimizing a CNN model using the HyperBand algorithm, then we would explore different configurations by varying the number of filters in the convolutional layers. This hyperparameter determines the number of filters or kernels that are applied to the input data during the convolution operation. Thus, for a specific experiment, we might try using 128 filters in one configuration, 160 filters in another, and so on up to 256 filters. Each configuration would be evaluated to determine its performance on the given dataset, and the algorithm would iteratively search through these options to find the best-performing combination of hyperparameters. Table 2 displays the search space of the hyperparameters considered in the optimization process. Due to the extensive range of hyperparameters, the optimization algorithm necessitated a total of 9 hours to achieve optimal results. Generally, random search outpaces grid search in speed but often sacrifices performance. However, in this study, a novel approach to random search, termed the HyperBand hyperparameter optimization technique, was leveraged. This innovative method facilitated the attainment of a high level of accuracy in a relatively brief timeframe. Having max pooling layers and dropout layers after every Conv1D layer also meant that the number of Conv1D layers was the same as the number of max pooling layers and dropout layers. But the hyperparameters for the layers and the number of layers changed throughout the hyperparameter optimization procedure. After the best hyperparameters were found using the Hyperband, the model had been trained with the best hyperparameters. In this study, the presence of accelerometers on every story of the building facilitated comprehensive data collection for structural health monitoring. However, constraints such as scarcity of accelerometers or technical limitations may necessitate working with fewer sensors in some scenarios. Consequently, to address this variability in data availability, separate neural networks were trained using data from single accelerometers in addition to the complete dataset. This approach enabled a comparative analysis of the accuracy levels between the models trained on the entire dataset and those trained on subsets with fewer sensors. To ensure a fair comparison among these neural networks, the search space of the hyperparameters, optimization type, and other relevant parameters remained constant across all experiments. Detailed results, including the accuracy and loss values for each combination of hyperparameters and datasets, are provided in the Appendices A-C for thorough examination and comparison. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Search Space** \\ \hline Number of filters & 128, 160, 192, 224, 256 \\ Conv1D layers & 1, 2, 3, 4, 5, 6 \\ Dropout & 0.1, 0.2, 0.3, 0.4, 0.5 \\ Dense layers & 1, 2, 3, 4 \\ Number of units in dense layer & 128, 256, 512, 1024, 2048 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Search space for the HyperBand algorithm.