Smart Structural Health Monitoring of Civil Infrastructures Using AI-Powered Wireless Sensor Networks
Keywords:
Artificial intelligence, Civil infrastructure, Machine learning, Predictive maintenance, Structural health monitoring, Wireless sensor networksAbstract
Structural health monitoring (SHM) has become a cornerstone in the management and preservation of civil infrastructure, especially as urban development accelerates and structures age. The safety, resilience, and functionality of critical infrastructure such as bridges, tunnels, dams, and skyscrapers depend on early detection of damage and timely intervention. Traditional SHM approaches, while effective in some contexts, suffer from limitations related to scalability, cost, and real-time analysis. The integration of artificial intelligence (AI) with wireless sensor networks (WSNs) marks a paradigm shift in SHM. WSNs enable the widespread deployment of low-power, autonomous sensors that collect real-time structural data. When combined with AI algorithms such as machine learning and deep learning, these systems become capable of detecting anomalies, predicting structural failures, and autonomously recommending maintenance actions. This review systematically examines the state-of-the-art AI-powered WSNs for SHM, highlighting key technologies, applications, challenges, and emerging trends. By analyzing recent advancements in AI-powered sensor systems, we examine how machine learning, deep learning, and sensor fusion techniques contribute to early damage detection, structural assessment, and predictive maintenance. The paper discusses existing systems, challenges, and future trends in this multidisciplinary domain. The findings demonstrate the potential of smart SHM systems to enhance decision-making, reduce maintenance costs, and improve the longevity and safety of civil infrastructure.
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