## 1 Introduction  
The Fukushima nuclear power plant accident, triggered by the Great East Japan Earthquake in 2011, marked a significant turning point in highlighting the importance of seismic safety in nuclear power plant (NPP) structures. In Korea, several recent earthquakes have damaged building structures, notably the 2016 Gyeongiu earthquake near Wolseong Nuclear Power Plant Units 1 to 4, which were manually shut down after the quake, thereby amplifying public interest and concerns regarding the seismic safety of nuclear facilities. Various methods are employed to assess the safety of NPP structures against earthquakes. In recent years, seismic probabilistic risk assessment (SPRA) has become a widely adopted methodology [1, 2]. In the SPRA approach, seismic safety assessments are conducted by integrating the earthquake hazard curve specific to the nuclear power plant's site with the structure's seismic fragility curve or that of installed equipment [3]. This integration necessitates conducting multiple time history analyses of NPP structures under various seismic loads. To facilitate these analyses, commercial finite element analysis programs such as Abaqus are often utilized. However, because the finite element models of NPP structures consist of numerous elements and nodes, time history analyses are both time-consuming and resource-intensive. In response to this, ongoing research is exploring various methods to predict the seismic response of nuclear power plant structures efficiently, aiming to minimize computational resources and time while maintaining accuracy. [PERSON] et al. [4] performed a multivariate seismic fragility analysis of reinforced concrete squat shear walls, leveraging surrogate models to derive a simplified closed-form equation for a specific surrogate seismic demand model. Multiple methodologies, such as the response surface method, support vector machines, Gaussian process regression, and neural networks, are employed to construct and test these surrogate models. [PERSON] and [PERSON] [5] developed fragility curves using Monte Carlo simulation with high computational efficiency, facilitated by the ensemble neural network. [PERSON] et al. [6] applied classification-based machine learning tools to predict structural responses and fragility curves, exploring ten different methods in this area. [PERSON] et al. [7] utilized well-known machine learning techniques to develop a predictive model for the seismic response and performance assessment of reinforced concrete moment-resisting frames. As evidenced by the recent studies, machine learning (ML) techniques have been progressively employed in different areas of structural engineering [8]. They are particularly becoming prevalent as alternative approaches to complex finite element analysis models for predicting structural responses [9]. Although these studies have mostly addressed relatively simple building structures, the development of machine learning-based seismic response prediction models for more complex nuclear power plant structures is still in its early stages. Several recent studies have demonstrated the use of machine learning techniques in structural safety assessments. For example, [PERSON] et al. [10] employed four machine learning techniques to derive seismic fragility curves for non-engineered masonry structures using an extensive dataset collected from 646 masonry walls in Malawi. The study demonstrated that ML models could accurately predict the probability of structural collapse based on peak ground acceleration (PGA). This research highlights the potential of ML to efficiently generate fragility curves for regions lacking detailed structural models, offering a data-driven alternative to conventional numerical methods. [PERSON] et al. [11] presented a comprehensive review of ML applications for structural response prediction in the context of structural health monitoring (SHM) for civil engineering structures.