## 5 Discussion
### Implications for Infrastructure Management  
The findings of this study have significant practical implications for the management and maintenance of RC structures in seismic zones. The observed increase in seismic vulnerability due to corrosion over time, particularly for moderate and intensive corrosion levels, underscores the need for more proactive and adaptive management strategies. Various protection techniques that are already in practice to mitigate corrosion in reinforced concrete structures [81] should be more commonly used. In addition to the traditional methods and strategies for managing and mitigating corrosion in RC structures, the use of machine learning (ML) presents a promising avenue for enhancing the accuracy and efficiency of corrosion assessment. Recent studies have demonstrated that ML algorithms can significantly improve the prediction of corrosion progression, structural capacity, and overall durability of RC structures [82; 83; 84]. Furthermore, the use of machine learning (ML) has demonstrated significant potential in engineering scenarios where data availability is limited, and decisions must be made with sparse or incomplete information [85; 86].  
Infrastructure managers should consider implementing more frequent inspections and condition assessments, especially for structures in corrosive environments. Furthermore, the implementation of novel technologies for the early detection and continuous monitoring of corrosion is essential for effectively preventing or mitigating corrosion-related failures [87]. The cumulative risk ratios presented in this study provide a quantitative basis for adjusting maintenance schedules and allocating resources more effectively. For instance, structures in highly corrosive environments may require major interventions or retrofitting much earlier in their lifecycle than previously anticipated.  
Moreover, our results highlight the critical importance of incorporating corrosion effects into long-term seismic risk assessments. Traditional seismic risk assessments that do not account for time-dependent degradation may significantly underestimate the vulnerability of structures, particularly in the latter half of their design life. This underestimation could lead to inadequate preparedness and potentially catastrophic consequences in the event of a major seismic event. By considering corrosion effects, infrastructure managers can develop more realistic and comprehensive risk management strategies, ensuring the resilience of critical infrastructure over its entire lifecycle.  
From a methodological perspective, this study contributes a novel approach to integrating corrosion effects into seismic fragility curves. The proposed framework allows for a dynamic adjustment of fragility parameters based on corrosion progression, providing a more accurate representation of structural vulnerability over time. This integration of time-dependent degradation into seismic risk assessment represents a significant advancement over traditional risk assessment models. A key strength of our approach is its ability to quantify the cumulative effects of corrosion on seismic risk over extended periods, as demonstrated by the cumulative risk ratios. This framework provides a comprehensive view of lifecycle risk, aiding researchers and practitioners in making more in-formed decisions regarding maintenance, retrofitting, and resource allocation to enhance infrastructure resilience in seismically active and corrosive environments.