Infrastructure Maintenance Models explores innovative approaches to maintaining and enhancing critical urban systems. It emphasizes data-driven decision-making and proactive strategies to combat the risks associated with aging infrastructure, such as water main breaks or bridge collapses. The book highlights the significance of understanding infrastructure lifecycles and adapting maintenance strategies to modern challenges like climate change and technological disruption.

One intriguing fact is the emphasis on using machine learning to predict infrastructure deterioration, allowing for optimized maintenance schedules. Another key insight involves balancing preventive and reactive maintenance for cost-effective management.

The book presents a comprehensive framework, beginning with fundamental concepts like asset inventory and condition assessment. It then delves into advanced modeling techniques, including statistical analysis and AI applications, for predicting infrastructure performance. The final part focuses on practical implementation strategies, such as risk management and cost-benefit analysis.

The book's value lies in its integrated approach, combining advanced modeling with real-world applications, providing a novel perspective on leveraging data and technology for improved infrastructure decision-making in civil engineering and urban planning.