AI Tech Revolutionizes Nuclear Plant Gear Checks

Abstract

Critical equipment in nuclear power plant auxiliary buildings such as control cabinets, panels, transformers, and diesel generators often malfunction before structural damage occurs, demanding rapid post-earthquake inspection prioritization. However, direct walkdown inspection or dense sensor networks are impractical due to restricted accessibility in radiological zones and the high costs associated with maintenance. To address this, we propose a residual convolutional network-based virtual sensing framework that supports urgent inspection prioritization by predicting acceleration at 139 locations from a single high-quality seismometer. The model employs six residual blocks with progressively downsized kernels to capture multi-scale features, while skip connections prevent vanishing gradients. Trained on artificial earthquakes with 10 dB noise and validated against unseen Next Generation Attenuation-West 2 ground motions matched to Nuclear Regulatory Commission Regulatory Guide 1.60 and Korean uniform-hazard spectra, the model achieves a maximum mean absolute percentage error of 0.44%-0.59% for noise-free case and ≤4.23% at 10 dB, demonstrating robust generalization. The resulting rapid, noise-tolerant virtual sensor network enables actionable equipment-level decision making in nuclear facilities at a fraction of conventional monitoring cost.

Electrical equipment housed within auxiliary buildings of nuclear power plants-such as control panels, transformers, and emergency generators-are particularly vulnerable to vibrations. Notably, during the 2016 Gyeongju earthquake, while concrete structures remained intact, power facilities had to be shut down for safety inspections. Now, a new technology allows for quick identification of equipment requiring maintenance without the need for extensive manual inspections.

Researchers from the Department of Civil, Urban, Earth, and Environmental Engineering at UNIST, led by Professor Young-Joo Lee, in collaboration with Dr. Jaebeom Lee from the Division of Physical Metrology at the Korea Research Institute of Standards and Science, announced the development of an advanced artificial intelligence (AI) model capable of estimating seismic responses at 139 detailed points within auxiliary buildings of nuclear power plants.

Figure 1. Overview of the virtualizing scheme of physical sensors in nuclear power plants [NPPs].

This AI model can analyze seismic data captured by a single high-quality sensor and, within 0.07 seconds, predict the acceleration response at 139 locations inside the building. These acceleration responses indicate how strongly and quickly equipment is shaken during an earthquake, helping prioritize which areas and devices require urgent inspection.

While installing hundreds of sensors would be necessary for direct measurement, this AI acts as a virtual sensor, accurately estimating responses across multiple points without physical sensors. This significantly reduces maintenance costs and minimizes disruptions to plant operations.

The research team designed the AI with six residual convolutional blocks, enabling it to learn a wide range of vibration patterns-from slow ground motions to rapid tremors. As a result, the model can precisely estimate both large-scale movements of the entire structure and amplified vibrations near critical equipment.

In tests without noise, the model's prediction errors were as low as 0.44-0.59%. Even under artificially induced noise at 10dB, it maintained a low error rate of around 4%. Validation against real earthquake records (NGA-West 2 dataset) confirmed the model's reliability, even under seismic conditions set by safety standards used in Korea and the United States for nuclear facilities.

Professor Young-Joo Lee commented, "This technology drastically reduces inspection time, operational downtime, and maintenance costs for nuclear plants. Particularly in radiation-controlled zones where sensor installation and upkeep are highly restricted and expensive, this solution offers a fundamental improvement."

The significance of this breakthrough has been recognized internationally. First author Jingoo Lee received an honorable mention for the Shitaba Early Career Award at the 28th International Conference on Structural Mechanics in Reactor Technology (SMiRT28), held in Toronto, Canada, from August 10 to 15, 2025. SMiRT is a leading global conference specializing in reactor structural integrity and seismic safety.

The research findings were published in Computer-Aided Civil and Infrastructure Engineering on September 9, 2025. This project was supported by the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT).

Journal Reference

Jingoo Lee, Seungjun Lee, Young-Joo Lee, and Jaebeom Lee, "Virtual sensing of seismic floor responses for rapid prioritization of critical equipment inspection in nuclear power plants," Comput.‐Aided Civ. Inf., (2025).