## 6 Conclusions  
This research introduces an innovative framework for evaluating the seismic risks associated with reinforced concrete structures within critical infrastructure, accounting for the progressive effects of corrosion. Our findings reveal a significant and time-dependent increase in seismic vulnerability due to corrosion, particularly under moderate and intensive corrosion conditions. The cumulative risk ratios demonstrate that over a 120-year lifespan, structures in highly corrosive environments may face up to six times the baseline seismic risk, depending on the location and corrosion intensity.  
A key novelty of this study is that it can serve as a tool for real-time monitoring by incorporating our approach to adjusting fragility parameters based on corrosion progression. This approach offers a more realistic and accurate representation of structural vulnerability as it evolves, enabling a comprehensive lifecycle risk assessment. The framework's capacity to quantify the cumulative impact of corrosion on seismic risk provides a valuable tool for long-term infrastructure resilience planning. As demonstrated in the case study, applying corrosion-adjusted fragility curves allows for a more detailed understanding of risk escalation across different geographic locations and environmental conditions.  
Furthermore, this study underscores the critical importance of incorporating time-dependent degradation into seismic risk assessments and the need for more proactive and tailored approaches to infrastructure management. The study's findings call for more frequent structural health monitoring, earlier intervention strategies, and the integration of time-dependent seismic vulnerability assessments in infrastructure planning and budgeting.  
In conclusion, this study provides a comprehensive evaluation of substation resilience under seismic conditions, focusing on the critical components' operational capacity and failure probability. The development of quantitative metrics and vulnerability curves offers valuable insights into the behavior of substations during seismic events, and the proposed models contribute to a deeper understanding of post-earthquake functionality. Future research should refine these models by integrating more complex factors essential for accurately predicting substation performance in seismic events. Additionally, experimental validation using real-world data will greatly enhance the models' accuracy and practical applicability.  
Conceptualization, I.M.S., A.U. and G.L.S.; methodology, I.M.S., G.L.S. and A.U.; software, A.U. and G.L.S.; validation, I.M.S., G.L.S. and A.U.; formal analysis, G.L.S. and A.U.; investigation, G.L.S. and A.U.; resources, I.M.S.; data curation, G.L.S. and A.U., writing--original draft preparation, G.L.S. and A.U.; writing--review and editing, I.M.S.; visualization, A.U. and G.L.S.; supervision, I.M.S.; project administration, I.M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We acknowledge the Ariel HPC Center at Ariel University for providing computing resources that contribute to the research results reported in this paper. Special thanks are extended to [PERSON] and [PERSON] for their consistent support and expertise in HPC and its applications.  
The authors declare no conflicts of interest.