AI-Enabled Structural and Façade Health Monitoring for Resilient Cities

Abstract

Urbanization and climate change place increasing strain on built environments, making resilience a priority for modern cities. Structural and façade failures in buildings pose safety risks, economic losses, and environmental impacts. Conventional inspection methods are time-consuming, costly, and prone to human error. This paper presents an AI-enabled framework for real-time structural and façade health monitoring that leverages computer vision, sensor networks, and predictive analytics. By integrating deep learning algorithms with Internet of Things (IoT) infrastructure, the proposed system detects early signs of material degradation, cracks, and façade detachment. Experimental validation demonstrates improved detection accuracy, reduced inspection time, and enhanced predictive maintenance capabilities. The results indicate that AI-enabled monitoring can significantly enhance the safety, efficiency, and resilience of urban infrastructure.