AI-Based Structural Health Monitoring Systems for Resilient and Sustainable Infrastructure

Abstract

Structural deterioration and unexpected failures in infrastructure systems pose significant risks to public safety, economic stability, and environmental sustainability. Artificial intelligence has emerged as a transformative tool for structural health monitoring by enabling data-driven damage detection, condition assessment, and predictive maintenance. This paper presents an integrated AI-based structural health monitoring framework designed to enhance infrastructure resilience and sustainability. The proposed system combines sensor networks, machine learning algorithms, and real-time data analytics to identify structural anomalies and predict failure progression. Analytical evaluation demonstrates improved detection accuracy, reduced inspection costs, and enhanced lifecycle performance compared to traditional monitoring techniques.