## 1 Introduction
It highlights the growing role of ML, deep learning (DL), and meta-heuristic algorithms in predicting structural behavior. The paper emphasizes that these methods can achieve acceptable prediction accuracy while offering increased speed, efficiency, and adaptability compared to conventional approaches. In a related study, [PERSON] et al. [12] presented a comprehensive literature review on the use of soft computing techniques for rapid visual screening (RVS) of existing buildings. They highlighted the limitations of conventional seismic assessment methods for large inventories and emphasized the value of machine learning and probabilistic approaches in enabling scalable and uncertainty-tolerant vulnerability evaluation. [PERSON] et al. [13], on the other hand, developed a DL-based model that achieved high-accuracy damage detection in steel structures using multi-scale convolutional layers, and attention mechanisms. Their model demonstrated strong adaptability and robustness across various structural types and seismic conditions, offering practical potential for next-generation SHM systems. [PERSON] et al. [14] assessed the seismic vulnerability of RC and unreinforced masonry school buildings using ML models such as XGBoostand Random Forest, achieving high prediction accuracy for fragility curves based on IDA results. [PERSON] et al. [15] developed an ML-based framework combining classification and regression models to predict damage in RC frames, employing stacking ensemble methods to address epistemic uncertainty. [PERSON] et al. [16] proposed an integrated ML procedure using neural networks and fuzzy inference systems for qualitative seismic design and assessment, which was validated against nonlinear time history analysis. [PERSON] and [PERSON] [17] evaluated several ML algorithms using real earthquake damage data from Nepal to predict seismic damage of RC buildings, demonstrating the models' applicability in post-disaster response scenarios. In the nuclear domain, [PERSON] et al. [18] applied computer vision and ML methods to measure component dimensions in difficult-to-access NPP environments, focusing on precision reconstruction under operational constraints. [PERSON] et al. [19] provided a broad review of AI-based fault detection and diagnosis techniques in NPPs, emphasizing their advantages over traditional physics-based methods. [PERSON] et al. [20] proposed a machine learning-driven probabilistic seismic demand model (PSDM) for NPPs using multiple intensity measures, showing improved accuracy and reduced analysis time through ML-assisted fragility modeling. This study simplified the NPP by building a lumped-mass stick model of the containment and secondary system. [PERSON] et al. [21] introduced a damage-state-based fragility analysis approach for NPPs that accounts for structural uncertainties and demonstrated the utility of explainable ML techniques in sensitivity analysis. However, these studies did not explicitly consider material degradation due to aging, which is a critical factor in long-term seismic performance of NPP structures. In addition to structural performance prediction, several studies have explored the application of machine learning to material degradation modeling. [PERSON] et al. [22] used ML algorithms to predict concrete aging factors based on material and environmental parameters, highlighting the ability of advanced models like LightGBM to achieve high accuracy without extensive physical testing. [PERSON] et al. [23] reviewed the role of ML in concrete research, discussing its applications, limitations, and best practices for mixture design and durability modeling under increasingly complex cementitious systems. Although these studies offer important contributions to understanding concrete aging at the material level, they do not directly address seismic response prediction at the structural scale. Nuclear power plant structures, typically constructed from thick reinforced concrete (RC), are designed to robustly prevent the leakage of radioactive materials.