## 3 Construction of Training and Test Datasets for Machine Learning Model  
A database was developed to train and validate a machine learning model for predicting the seismic response of the example NPP structure. The input for the machine learning model includes seismic load characteristics and material properties accounting for aging deterioration effects, while the output was defined as the floor acceleration response of the target story. The inputs and outputs of the database are illustrated in Figure 6.  
Figure 4: Acceleration response spectra for 100 selected earthquakes. Figure 5: Time history of ground acceleration for a simulated earthquake. As shown in the figure, 13 earthquake intensity measures were employed to characterize the seismic load. These measures include four amplitude-related metrics, such as PGA and peak ground velocity (PGV); four energy-related metrics, including Arias intensity (IA) and root mean square acceleration (ARMS); and five frequency-related metrics, such as spectral acceleration (Sa) and average spectral acceleration (ASA). Details of all 13 earthquake intensity measures used in this study are provided in Table 1.  
Because the example NPP structure is situated in regions with low-to-moderate seismic activity, its structural response to seismic loads is assumed to be elastic. Consequently, linear elastic time history analyses were conducted to determine its seismic responses. The linear elastic structural analysis incorporated variations in elastic modulus, Poisson's ratio, density, and damping ratio to account for aging deterioration effects on the structural properties of reinforced concrete materials. \begin{table}
\begin{tabular}{c c c c} \hline
**Type** & **No.** & **Intensity Measures** & **Definition** \\ \hline \multirow{4}{*}{Amplitude-related} & 1 & PGA (Peak Ground Acceleration) & max \(\mid\) a(t) \(\mid\) \\  & 2 & PGV (Peak Ground Velocity) & max \(\mid\) v(t) \(\mid\) \\  & 3 & SMA (Sustained Max. Acceleration) & The third highest acceleration \\  & 4 & SMV (Sustained Max. Velocity) & The third highest velocity \\ \hline \multirow{4}{*}{Energy-related} & 5 & IA (Arias Intensity) & \(\frac{\pi}{2g}\int_{0}^{lat}a(t)^{2}dt\) \\  & 6 & ARMS (Root Mean Square & \(\sqrt{\frac{1}{lat}\int_{0}^{lat}a(t)^{2}dt}\) \\  & 7 & VRMS (Root Mean Square Velocity) & \(\sqrt{\frac{1}{lat}\int_{0}^{lat}v(t)^{2}dt}\) \\  & 8 & CAV (Cumulative Absolute Velocity) & \(\int_{0}^{lat}|a(t)|dt\) \\ \hline \multirow{4}{*}{Frequency-related} & 9 & Sa (Spectral Acceleration) & \(S_{a}(f_{0})\)\(f_{0}\) = 10 Hz, damping 5\% \\  & 10 & ASA (Average Spectral Acceleration) & \(\int_{9.5}^{10.5}S_{a}(f)df\)\(f\): frequency \\ \cline{1-1}  & 11 & TP (Predominant Period) & \(\operatorname*{argmax}_{T}S_{a}(T)\)\(\operatorname{T}=1/f\) \\ \cline{1-1}  & 12 & ASI (Acceleration Spectrum Intensity) & \(\int_{0.1}^{0.5}S_{a}(T)dT\) \\ \cline{1-1}  & 13 & VSI (Velocity Spectrum Intensity) & \(\int_{0.1}^{2.5}S_{v}(T)dT\) \\ \hline \end{tabular}
\end{table} 
Table 1: Earthquake intensity measures indicating seismic characteristics. Figure 6: Input and output features of the ML model. The material propertiemaxs used in the analysis adhered to a log-normal distribution. Table 2 presents the median values and coefficients of variation for these properties. Based on prior research on the impact of aging deterioration on RC materials [29; 30; 31; 32], variability assumptions included an elastic modulus change of up to approximately 20%, a density change of about 5%, a damping ratio alteration of around 40%, and a Poisson's ratio adjustment of approximately 10%. These parameters helped generate a dataset for material properties, enabling the training of a machine learning model to accurately reflect changes due to aging deterioration. Although environmental factors such as carbonation, neutralization, temperature variation, and radiation exposure were not explicitly modeled, their effects were indirectly considered through adjustments in material property inputs.