## 5 Performance Evaluation of Seismic Response Prediction of ML Models  
Employing the six machine learning algorithms described earlier, a seismic response prediction model was created for the example NPP structure, incorporating aging deterioration. To assess the prediction performance of the developed ML model, 20% (100 samples) of the dataset not used during training were selected. The predicted seismic response values of the ML models, accounting for aging effects, were compared with the target values as shown in Figure 11. In the graph, the horizontal axis denotes the predicted responses, while the vertical axis corresponds to the results from the finite element analysis model, i.e., the target values. High prediction accuracy is evident when the result dots closely align with the diagonal dashed line in the graph. The figure reveals that the prediction results of the DT model are the most dispersed along the diagonal, suggesting that it has the lowest prediction accuracy among all the models. In contrast, the RF model, which employs multiple DT models, displays predictions that are closer to the diagonal compared to the DT model. The SVM model demonstrated superior prediction performance within a limited seismic response range relative to the RF model. However, the kNN model showed decreasing accuracy as the seismic response increased. The XGBoost model performed slightly better than the kNN model, while the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Optimal Value** & **Search Range** & **Step/Options** \\ \hline n\_neighbors & 2 & 1–10 & Step = 1 \\ weights & distance & [uniform, distance] & categorical \\ algorithm & ball\_tree & [auto, ball\_tree, kd\_tree, brute] & categorical \\ leaf\_size & 10 & 10–50 & Step = 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Hyperparameters for the XGBoost model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Optimal Value** & **Search Range** & **Step/Options** \\ \hline hidden layer & 3 & 1–3 & Step = 1 \\ node number & (150, 150, 100) & 50–150 per layer & Step = 50 \\ solver & lbfgs & [adam, sgd, lbfgs] & categorical \\ max-iter & 10,000 & 1000–20,000 & Step = 5000 \\ alpha & 0.0001 & \([1\times 10^{-5},1\times 10^{-4},\) \\  & & \(1\times 10^{-3},1\times 10^{-2}]\) & log-scale steps \\ activation & ReLU & [ReLU, tanh, logistic] & categorical \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Hyperparameters for neural network model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Optimal Value** & **Search Range** & **Step/Options** \\ \hline kernel & poly & [linear, poly, rbf] & categorical \\ C & 1 & 0.1–10 & [0.1, 5, 10] \\ epsilon & 0.1 & 0.05–0.5 & Step = 0.05 \\ gamma & 1.5 & 0.5–3.0 & Step = 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyperparameters for the Support Vector Machine model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Optimal Value** & **Search Range** & **Step/Options** \\ \hline n\_neighbors & 2 & 1–10 & Step = 1 \\ weights & distance & [uniform, distance] & categorical \\ algorithm & ball\_tree & [auto, ball\_tree, kd\_tree, brute] & categorical \\ leaf\_size & 10 & 10–50 & Step = 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hyperparameters for the k-Nearest Neighbors algorithm. NN model achieved the highest overall accuracy, with its prediction results being the most concentrated along the diagonal. It can be observed from the figure that the prediction results are clustered around the diagonal in areas where the structural response is minimal, indicating high prediction accuracy. However, as the response magnitude increases, the results increasingly deviate from the diagonal, leading to reduced accuracy. This phenomenon can be elucidated by examining the distribution of the established database, as depicted in Figure 7. The database distribution suggests that the machine learning model was inadequately trained for strong seismic loads.