A research team, led by Professor Young-Joo Lee of the Department of Civil, Urban, and Environmental Engineering at UNIST and Senior Research Scientist Jaebeom Lee of the Korea Research Institute of Standards and Science (KRISS) has developed a deep-learning model for post-earthquake assessment of nuclear power plants. The model predicts seismic responses across the structure and estimates which locations may require priority inspection.

Post-earthquake assessment can be challenging when sensors are installed at only a limited number of locations. Expanding sensor coverage throughout a nuclear power plant is difficult because of cabling, regulatory, and maintenance constraints. Numerical simulations can fill these gaps, but repeated finite element analyzes become computationally expensive when uncertainties in structural properties are taken into account.

The researchers developed a deep-learning surrogate model that combines seismic ground motion with structural parameters to predict acceleration responses at 139 locations. This allows responses to be estimated at locations without physical sensors while reducing the computational cost of conventional analysis.

The model also accounts for uncertainty in structural properties. Rather than providing only a binary assessment, it calculates the probability that the response at each location will exceed a predefined equipment-level threshold. These probabilities can help engineers prioritize locations for further inspection.

Experimental validation showed an average maximum mean absolute percentage error of 1.37%, with R² values above 0.98. The model generates full acceleration time histories across multiple locations, allowing probabilistic responses to be assessed without repeatedly running computationally intensive finite element simulations.

The team also developed a method for configuring the AI architecture according to a structure's natural frequencies. The resulting model achieved accuracy comparable to a deep-learning model with more than 200 times as many parameters, while requiring substantially fewer computational resources.

The researchers expect the approach could also be adapted to other facilities that require rapid post-earthquake assessment, including semiconductor plants, data centers, and industrial facilities.

“For safety applications, accurate prediction alone is not enough; it is also important to understand the uncertainty in those predictions,” the researchers said. “By quantifying that uncertainty, the framework can provide additional information for experts deciding where further inspection is needed.” 

The research was conducted jointly by the teams of Senior Research Scientist Jaebeom Lee at KRISS, with Jingoo Lee as first author. The work was supported by the National Research Foundation of Korea and KRISS. The findings have been reported in three papers, including a study published in Reliability Engineering & System Safety.

The study was conducted jointly by the UNIST and KRISS research teams, with Jingoo Lee as first author. The work was supported by the National Research Foundation of Korea (NRF) and KRISS. The findings have been reported in three papers, including a study published in Reliability Engineering & System Safety (IF: 13.7).

Journal Reference

Jingoo Lee, Seungjun Lee, Young-Joo Lee, and Jaebeom Lee, “Predicting seismic floor response for nuclear power plant structures with time-series uncertainty propagation using attention-enhanced multimodal deep learning,” Reliab. Eng. Syst. Saf.,  (2026).