Predictive Framework for Optimizing Maintenance Schedules in Aging Public Infrastructure Systems
DOI:
https://doi.org/10.65150/EP-gjetr/V1E3/2025-06Keywords:
Predictive maintenance, infrastructure deterioration, asset management, machine learning, condition monitoring, optimization, digital transformation, lifecycle cost reduction, infrastructure resilience.Abstract
Aging public infrastructure systems—including transportation networks, water distribution facilities, and energy grids—face escalating deterioration due to increased usage, environmental stressors, and delayed maintenance interventions. Traditional maintenance approaches, often reactive and schedule-based, result in elevated lifecycle costs, reduced service reliability, and heightened risks of system failure. This study proposes a predictive framework for optimizing maintenance schedules in aging infrastructure by integrating condition-based monitoring, advanced computational modelling, and machine learning-driven decision support. The framework incorporates real-time sensor data, historical performance records, and environmental exposure variables to develop predictive degradation models capable of forecasting component failure probabilities. Multi-criteria optimization algorithms are employed to simultaneously minimize maintenance costs, downtime, and safety risks while enhancing asset longevity and performance reliability. The decision-support architecture enables dynamic prioritization of interventions in resource-constrained contexts, ensuring that maintenance actions generate maximum operational and economic benefits. Case-study simulations demonstrate that the predictive framework can reduce emergency repairs, extend service lifespans, and achieve substantial reductions in annual maintenance expenditures compared to conventional strategies. Furthermore, the system is adaptable across infrastructure types and scalable to regional and national asset portfolios. By shifting from reactive to proactive management paradigms, the proposed framework supports resilience-building objectives and informed policymaking for sustainable infrastructure governance. This research contributes to ongoing digital transformation efforts in civil infrastructure management and underlines the critical role of data-driven analytics in preserving aging public assets amid increasing climate-induced stresses. Future work should investigate cross-system interoperability, integration with digital twins, and stakeholder-centered implementation strategies to ensure widespread practical adoption.
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