## 7 Conclusions  
In this study, seismic response prediction ML models for a nuclear power plant structure, accounting for aging deterioration, were developed, and the prediction performance of each model was evaluated. The OPR1000 auxiliary building was selected as the example structure, and 500 artificial ground accelerations were generated. Time history analyses were conducted to create datasets for training and testing of the ML models. To incorporate the effects of aging on reinforced concrete structures, changes in material properties such as elastic modulus, Poisson's ratio, density, and damping ratio were considered as input variables in building the database. Thirteen widely used earthquake intensity measures were also utilized as input variables to represent the characteristics of the artificial seismic loads, while the floor response spectrum was selected as the output. Seismic response prediction models for the example NPP structure, considering aging deterioration, were developed using six machine learning algorithms commonly applied in structural engineering. Among these, the DT model demonstrated the lowest prediction  
Figure 19: Prediction performance of the PLS-NN model trained with a dataset of 200: (**a**) prediction results of the model with 9 components; (**b**) prediction results of the model with 5 components. accuracy, with a coefficient of determination (R\({}^{2}\)) of 0.81. The kNN and XGBoost models showed improved accuracy, achieving R\({}^{2}\) values of 0.84 and 0.88, respectively. The RF and SVM models both achieved R\({}^{2}\) values of 0.90, indicating better prediction performance compared to the previous three models. Finally, the NN model exhibited the best prediction performance, with a coefficient of determination of 0.94. Since each time history analysis for constructing the database takes 3 to 4 h in this study, it is essential to minimize the dataset size required for training. To address this, feature selection and feature extraction techniques were applied to reduce the input dimension of the ML model. This study confirmed that a machine learning model with reduced input variables exhibited comparable prediction performance, even when trained with only 50% of the database required for the model incorporating all 17 input variables. Among the input dimensionality reduction techniques, the Partial Least Squares (PLS) method demonstrated the most effective performance. By reducing the 17 total input features to just 5 components, an efficient PLS-NN model was developed, significantly reducing database construction time while maintaining prediction accuracy. The purpose of this study is to establish a baseline ML framework for seismic response prediction in aging NPP structures. The trained model is intended for use in future seismic fragility assessments of the OPR1000 structure. The methodology and workflow proposed in this study can be adapted to other nuclear systems when appropriate structural models and seismic datasets become available. While this study focuses on the aging of structural material degradation, functional degradation, such as aging of control systems, sensor malfunctions, and equipment-level deterioration also plays a significant role in seismic responses. Therefore, future research will also try to integrate both material-level deterioration and functional degradation to more comprehensively assess the seismic performance of NPP systems under aging effects. Conceptualization, H.K. and S.L. ; methodology, S.L. and J.J.; software, H.K. and S.A.; validation, H.K. and S.A.; writing--original draft preparation, H.K. and J.J.; writing--review and editing, H.K., S.L., J.J. and S.A.; visualization, S.L. and J.J.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean government (MOTIE) (No. RS-2022-KP002849). Not applicable.