## 3 Construction of Training and Test Datasets for Machine Learning Model
For example, degradation of the elastic modulus caused by carbonation, reinforcement corrosion, or radiation can all be represented within a single framework by updating input parameters accordingly. This allows engineers to reflect site-specific deterioration patterns based on empirical data or design guidelines. While the model does not internally simulate environmental processes, it is well suited to capture their consequences, offering high adaptability across a broad range of aging scenarios. The overall data distribution was assessed using histograms of the 13 earthquake intensity measures for the 500 artificial ground motions created in this study, as depicted in Figure 7. The analysis confirms that all 13 input variables roughly follow a log-normal distribution, ensuring the database adequately represents the ground motion characteristics. This database aims to train the machine learning model effectively using ground motions from low and moderate seismic regions with varied characteristics. To determine the output values for the database, time history analysis results from the OPR100 auxiliary building, subjected to 500 artificial earthquake loads, were employed. The floor response spectrum (FRS) was calculated using the X and Y directional floor acceleration time histories recorded at four response positions per story, as shown in Figure 2. Spectral envelopes, indicating maximum FRS for each direction, were constructed from the FRS values at the four positions, as illustrated in Figure 8.  
The maximum FRS within the resonance frequency band of the MCC cabinet was calculated as the database output. For this study, the resonance frequency of the MCC cabinet was assumed to be 10 Hz. The database output was determined by integrating the floor acceleration spectrum over the resonance frequency range \(\pm\)5 Hz as depicted in Equation (1). \[y=\max_{i=X,Y}\int_{9.5}^{10.5}S_{a,i}(f)df \tag{1}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material Parameters** & **Distribution** & **Median** &
\begin{tabular}{c} **Coefficient of** \\ **Variation** \\ \end{tabular} \\ \hline Elastic modulus & Log-normal & 2.78 \(\times\) 10\({}^{4}\) & 0.2 \\ Poisson’s ratio & Log-normal & 0.17 & 0.1 \\ Density & Log-normal & 7.32–10.59 \(\times\) 10\({}^{-7}\) & 0.05 \\ Damping ratio & Log-normal & 0.05 & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variation of material parameters (Unit: N, mm). Figure 8: Example of a floor response spectrum: (**a**) X-direction response at height of 103 ft; (**b**) Y-direction response at height of 103 ft; (**c**) X-direction response at height of 84 ft; (**d**) Y-direction response at height of 84 ft; (**e**) X-direction response at height of 66 ft; (**f**) Y-direction response at height of 66 ft.  
Figure 7: Distribution of earthquake intensity measures. Here, Sa represents the floor spectral acceleration. Figure 9 illustrates the distribution of the database outputs calculated for 500 artificial seismic loads using Equation (1). As depicted, the output data follow an approximate log-normal distribution. Out of the 500 datasets, 80% were randomly selected for training, while the remaining 20% were allocated for testing ML models. In other words, the ML models were tested on unseen data, with 20% of the dataset set aside exclusively for evaluation. This test set was not used during training or hyperparameter tuning, ensuring that generalization performance was evaluated without bias. To analyze Pearson correlation between the input and output features of the constructed database, a correlation heatmap is presented in Figure 10. The figure shows that PGA, SMA, and ARMS all exhibit a correlation coefficient of 0.82, indicating a strong correlation. Sa and ASA also showed a relatively high correlation coefficient of 0.79. Overall, the correlation of acceleration-related indices was notably high, whereas the correlation of velocity-related indices was relatively low.