AI-Driven Predictive Maintenance Framework for Smart Infrastructure: Cost-Benefit Analysis and Implementation Strategies for Urban Asset Management

Urban infrastructure deterioration costs global economies over $3.6 trillion annually, yet traditional inspection regimes remain reactive and inefficient. This research develops an integrated machine learning framework for predictive maintenance of critical urban assets, combining computer vision-based defect detection with economic optimization models for maintenance scheduling. The system processes real-time sensor data, visual inspections, and historical maintenance records to predict infrastructure failures and optimize intervention timing across municipal asset portfolios.

Implemented across transportation networks and public facilities in three urban regions, our deep learning models achieved 89.3% accuracy in predicting pavement distress, bridge deterioration, and utility system failures 60-90 days before critical thresholds. Cost-benefit analysis demonstrates 34% reduction in lifecycle maintenance costs compared to time-based schedules, with payback periods under 18 months for sensor network investments. The framework integrates seamlessly with existing asset management systems, providing municipal engineers with risk-prioritized intervention schedules that balance safety requirements against budget constraints.

We introduce a novel "infrastructure health index" that translates complex condition data into actionable maintenance priorities for non-technical decision-makers. Pilot deployments show the system enables data-driven capital planning, extending asset lifespans while reducing emergency repairs by 47%. This approach addresses critical challenges in aging infrastructure management, offering municipalities a scalable solution for optimizing limited maintenance budgets while ensuring public safety in smart city environments.