Conceptual Framework for Smart Sensor–driven Predictive Maintenance in Infrastructure Management

Abstract

Smart sensors, predictive analytics, and machine learning (ML) are reshaping how infrastructure is managed by enabling real-time, data-driven decision-making. Rather than relying on reactive maintenance, these technologies support proactive strategies that improve efficiency, reduce costs, and enhance sustainability. Predictive models based on sensor data can anticipate potential failures, allowing timely interventions that extend the lifespan of infrastructure systems. Machine learning enhances these models by improving accuracy and adaptability in predicting maintenance needs, detecting anomalies, and optimizing system performance. This article provides a conceptual framework and narrative review of the integration of these technologies across key domains such as energy, transportation, and structural health monitoring. It highlights opportunities for improving safety, efficiency, and environmental outcomes while acknowledging persistent challenges related to data quality, scalability, and security. The discussion emphasizes the need for continued research on sensor networks, edge computing, and AI-driven decision support to advance smart infrastructure management and prepare for future applications in smart cities and large-scale industrial systems.