Machine Learning Applications in Sensor-Based Infrastructure Monitoring

Contemporary infrastructure management confronts unprecedented challenges arising from ageing systems, resource constraints, and escalating demands for efficiency and sustainability. This review examines the transformative potential of integrating smart sensor networks with predictive analytics and machine learning (ML) to address these challenges through data-driven, proactive management approaches. Smart sensors enable continuous, real-time monitoring of critical infrastructure parameters, including structural integrity, environmental conditions, and operational performance, thereby facilitating early detection of anomalies and potential failures. When combined with predictive analytics and ML algorithms—ranging from regression models and decision trees to neural networks and support vector machines—these sensor data streams enable infrastructure managers to transition from reactive maintenance strategies to predictive and preventive paradigms. This paper synthesises evidence from diverse applications across smart cities, structural health monitoring, and energy utilities, demonstrating substantial improvements in operational efficiency, cost reduction, and asset longevity. Case studies illustrate how predictive models optimise traffic flow, enhance grid reliability, detect pipeline leaks, and forecast structural deterioration. Whilst acknowledging persistent challenges related to data quality, system scalability, model interpretability, and cybersecurity, this review highlights the considerable promise of sensor fusion techniques, edge computing, and autonomous systems in advancing infrastructure management practices. The findings underscore that interdisciplinary collaboration and continued technological innovation are essential to realising fully intelligent, adaptive infrastructure networks capable of meeting the complex demands of urbanisation and sustainability in the twenty-first century.