Author(s): Søren Larsen

Abstract: Pavement maintenance is essential for ensuring in ensuring transportation safety, efficiency, and infrastructure sustainability. Traditional inspection-based pavement management systems are limited by infrequent assessments, delayed interventions, and escalating maintenance costs. This research investigates the implementation of an Intelligent Pavement Management System (IPMS) integrating Internet of Things (IoT) sensors to enable continuous, real-time monitoring and predictive maintenance of roadway infrastructure. A network of embedded and surface-mounted sensors comprising strain gauges, accelerometers, temperature and moisture sensors, and vibration detectors was deployed on an urban arterial road. Data were transmitted to a cloud-based platform and processed using advanced filtering and analytical algorithms. Statistical analysis revealed a strong agreement between IoT-derived Pavement Condition Index (PCI) and conventional inspection results (Pearson r ? 0.91, RMSE ? 5 PCI points). Operational outcomes demonstrated a reduction in emergency repairs by approximately 51%, an improvement in PCI values, a 20% reduction in annual maintenance costs, and a 12-day lead time in distress detection compared to routine inspections. The results confirm the feasibility and effectiveness of IoT-integrated pavement monitoring as a decision-support tool for transportation agencies. The study concludes that IPMS enhances the precision, timeliness, and cost-efficiency of maintenance operations and supports the transition toward predictive, data-driven infrastructure management. Practical recommendations include targeted sensor deployment, integration with existing asset management frameworks, workforce training, and adoption of standardized data and security protocols.

Pages: 12-17 | Views: 24 | Downloads: 19

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