International Journal of Engineering & Tech Development

E-ISSN: 3067-672X

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Volume 3 Issue 10
October 2026

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AI-Based Structural Health Monitoring for Modern Infrastructure

Author(s) Lucas Meijer
Country Netherlands
Abstract Structural health monitoring (SHM) has become an essential component of modern infrastructure management because aging structures, increasing environmental loads, traffic growth, and complex operating conditions require continuous assessment of structural performance. Conventional inspection practices frequently depend on periodic visual examinations and manually interpreted measurements, which may be costly, time-consuming, and inadequate for identifying rapidly developing structural anomalies. Artificial intelligence (AI) provides new opportunities to improve SHM through automated sensing, pattern recognition, predictive analytics, anomaly detection, and intelligent decision support. This paper examines AI-based approaches for structural health monitoring across modern infrastructure systems, including bridges, buildings, tunnels, transportation facilities, and other critical structures. The study reviews the integration of machine learning, deep learning, sensor networks, computer vision, and data-driven predictive models within SHM processes.
A conceptual analytical framework is developed to examine how AI can support data acquisition, structural condition assessment, damage identification, performance prediction, and maintenance decision-making. The analysis emphasizes the relationship between sensor quality, data processing, model accuracy, and practical monitoring outcomes. The findings indicate that AI-enabled SHM can improve early damage detection, reduce dependence on manual inspections, support predictive maintenance, and enhance infrastructure resilience. However, implementation remains constrained by limited high-quality structural datasets, sensor reliability, environmental variability, model interpretability, computational requirements, and cybersecurity concerns. Future development should therefore emphasize explainable AI, multimodal sensing, digital twins, edge intelligence, transfer learning, and standardized evaluation frameworks. AI-based SHM represents a promising pathway toward more proactive, efficient, and resilient infrastructure management when intelligent algorithms are integrated with engineering knowledge and reliable monitoring systems.
Keywords Artificial Intelligence, Structural Health Monitoring, Machine Learning, Infrastructure, Damage Detection, Predictive Maintenance, Sensor Networks, Structural Engineering
Field Engineering
Published In Volume 3, Issue 1, January 2026
Published On 2026-01-01
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E-ISSN: 3067-672X
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