In a nuclear power plant, it is essential to improve the seismic safety of the piping system for the coolant transfer to cool the high temperature caused by the nuclear reaction. Under this background, this study makes two major contributions. The first is that though tuned mass dampers (TMDs) were originally used only to reduce the vibration of piping itself, through this research, it was first proved that it had a positive effect on the improvement of the seismic performance of nuclear piping systems. Additionally, this study proposed a design approach that effectively obtains the optimal design values of TMDs associated with seismic performance. In order to effectively derive the TMD optimum design values, we not only utilized the existing TMD optimum design formula, but also additionally proposed a frequency response analysis-based TMD optimal design method. As a result, it was seen that primary responses of system were significantly reduced under the input seismic load due to the use of TMDs for the piping system. It was also confirmed that the use of the existing TMD formula brought about a similar degree of response reduction effect, while it was possible to get the improved effect when using the proposed method.

In the event of an earthquake, it is essential to accurately assess the seismic fragility of piping systems to ensure the continued safety of society. When evaluating the seismic fragility of a piping system, which is generally a secondary structural system, this should mainly be an integrated model that includes both the primary structural frames and the secondary ones, unlike the primary structural system of a building. Hence, the piping seismic fragility evaluation has an issue in that it takes considerable computational time because numerical analyses must be performed on a relatively complex model. Given this background, the purpose of this study is to propose an efficient piping seismic fragility analysis method by utilizing the existing seismic fragility analysis method and the Bayesian updating concept. In order to verify the validity of the proposed method, it was applied to a building–piping coupled structural system example, and its results were analyzed and compared with the results of the existing method in terms of accuracy and efficiency. As a result, the proposed method showed a similar accuracy compared with the existing method while significantly reducing the numerical cost of nonlinear seismic response analyses necessary for these results.

Probabilistic safety assessment (PSA) of nuclear facilities on external multi-hazards has become a major issue after the Fukushima accident in 2011. However, the existing external hazard PSA methodology is for single hazard events and cannot cover the impact of multi-hazards. Therefore, this study proposes a methodology for quantifying multi-hazard risks for nuclear energy plants. Specifically, we developed an efficient multi-hazard PSA methodology based on the probability distribution-based Boolean algebraic approach and sampling-based method, which are currently single-hazard PSA methodologies. The limitations of the probability distribution-based Boolean algebraic approach not being able to handle partial dependencies between the components are solved through this sampling-based method. In addition, we devised an algorithm that was more efficient than the existing algorithm for improving the limits of the current sampling-based method, as it required a significant computational time. The proposed methodology was applied from simple examples to single- and multi-hazard PSA examples of actual nuclear power plants. The results showed that the proposed methodology was verified in terms of accuracy and efficiency perspectives. Regarding the sampling-based method, it was confirmed that the proposed algorithm yielded fragility and risk results that have similar degrees of accuracy, even though it extracted a smaller number of samples than the existing algorithm.

The objective of this study is to propose a model that can predict the seismic performance of slope relatively accurately and efficiently by using machine learning methods. Probabilistic seismic fragility analyses of the slope had been carried out in other studies, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, the traditional statistical linear regression analysis showed a limit that could not accurately represent such nonlinear slope seismic performances. To overcome this limit, in this study, we used three machine learning methods (i.e., support vector machine (SVM), artificial neural network (ANN), Gaussian process regression (GPR)) to generate prediction models of the slope seismic performance. The models obtained through the machine learning methods basically showed better performance compared to the models of the traditional statistical methods. The results of the SVM showed no significant performance difference compared with the results of the nonlinear regression analysis method, but the results based on the ANN and GPR showed a remarkable improvement in the prediction performance over the other models. Furthermore, this study confirmed that the GPR-based model predicted relatively accurate seismic performance values compared with the model through the ANN.