## 1 Introduction
However, reinforced concrete is susceptible to aging deterioration from various factors, such as chloride penetration, carbonation, radioactivity, and thermal effects. This deterioration can change the properties of the structural material, potentially leading to rebar corrosion, loss of concrete and steel section, and overall degradation of performance [24]. In RC NPP structures, such aging deterioration can lead to changes in key material properties including elastic modulus, yield strength, density, and damping capacity. These modifications can influence the dynamic characteristics of the structure, thereby reducing its seismic resistance and altering its seismic responses [25]. Consequently, it is essential to consider these changes in material properties due to aging deterioration to accurately predict the seismic response of nuclear power plant structures. Building on this understanding, our study developed a seismic response prediction model for NPP structures that incorporates aging deterioration, utilizing machine learning techniques, and evaluated its accuracy. The OPR1000 (Optimized Power Reactor 1000 MW), a Korean standard nuclear power plant, was chosen as the target structure. Currently, a total of 10 OPR1000 units are either operational or under construction at the Gori, Wolseong, Hanbit, and Hanul nuclear power plant sites. The structure of the OPR1000 consists of two main components: the reactor containment building, which houses the reactor coolant system, and the reactor auxiliary building, which contains auxiliary system facilities vital for the operation of the reactor coolant system. The foundations of these buildings are isolated from each other. In regions with low-to-moderate seismic activity, such as Korea, reactor containment buildings are more prone to indirect damage caused by failures in auxiliary building facilities rather than direct structural damage from earthquakes. Therefore, this study focused on predicting the seismic responses of the OPR1000 auxiliary buildings using the developed machine learning model. To construct an effective machine learning model, it is critical to develop a comprehensive database for training and testing. To develop ML models capable of accurately predicting the seismic response of actual nuclear structures, a sufficient amount of real-world seismic response data from instrumented NPPs is essential. Unfortunately, such data are scarce in both quantity and diversity due to the inherent difficulty in collecting strong-motion response records from operating nuclear facilities. As a result, the majority of studies--including this study--have relied on simulation-based datasets constructed from finite element analyses. Therefore, time history analyses were performed using the finite element analysis model of the OPR1000 auxiliary building subjected to seismic loads. As part of the time history analysis, 500 artificial earthquake loads were generated based on the ground characteristics of the Ulsan area [26], a primary location for domestic nuclear power plants. The seismic responses of an NPP structure largely depend on the characteristics of the seismic loads and the properties of RC materials. Therefore, the machine learning model included input variables that reflect both seismic load characteristics and changes in material properties due to aging deterioration. In this study, several indicators representing the dynamic characteristics of ground motion time series data were considered. The widely used earthquake intensity measures, typically applied in seismic fragility analysis of structures and facilities, were chosen as input variables for the machine learning model. Additionally, to account for aging deterioration in RC material properties, changes in elastic modulus, Poisson's ratio, density, and damping ratio were also included as input variables in the ML prediction model.