<p>Seismic fragility analysis (SFA) is known as an effective probabilistic-based approach used to evaluate seismic fragility. There are various sources of uncertainties associated with this approach. A nuclear power plant (NPP) system is an extremely important infrastructure and contains many structural uncertainties due to construction issues or structural deterioration during service. Simulation of structural uncertainties effects is a costly and time-consuming endeavor. A novel approach to SFA for the NPP considering structural uncertainties based on the damage state is proposed and examined. The results suggest that considering the structural uncertainties is essential in assessing the fragility of the NPP structure, and the impact of structural uncertainties tends to increase with the state of damage. Subsequently, machine learning (ML) is found to be superior in high-precision damage state identification of the NPP for reducing the time of nonlinear time-history analysis (NLTHA) and could be applied in the damage state-based SFA. Also, the impact of various sources of uncertainties is investigated through sensitivity analysis. The Sobol and Shapley additive explanations (SHAP) method can be complementary to each other and able to solve the problem of quantifying seismic and structural uncertainties simultaneously and the interaction effect of each parameter.</p>