Road Pavement Digital Twin: A Blueprint with a Case Study

This paper presents the conceptualization and implementation of a comprehensive Road Pavement Digital Twin (RPDT) framework to enhance proactive pavement management. The blueprint outlines a multilayered architecture integrating real-time monitoring, hybrid physics-based and artificial intelligence (AI)-driven analytics, and decision-support tools in a unified system. It includes conceptual modeling, data pipelines, analytical workflows, applications, and real-world deployment. The RPDT was deployed on a 3 km section of Finland’s Räyskälän kantatie (KT54/3), integrating high-resolution data from strain gauges, temperature/moisture probes, ground penetrating radar, falling weight deflectometers, and crowdsourced telemetry. Data are processed via a secure edge–fog–cloud architecture, achieving sub-200 ms latency and over 98% ingestion success for seamless fusion and real-time analysis. The workflow incorporates automated synchronization, structural simulation, and deterioration forecasting using long short-term memory networks [mean absolute error (MAE) <5%] trained on the Long-Term Pavement Performance (LTPP) data set. Pavement distress is detected/segmented with deep learning models like You Only Look Once (YOLO)v11m (mAP 85%) and U-Net (IoU 93%), refined via closed-loop learning, and visualized on an interactive dashboard powered by Autodesk Platform Services. A multiobjective optimization module based on particle swarm optimization generates Pareto-optimal maintenance schedules balancing cost and condition. Compared to traditional methods, the RPDT yields 12–30% lifecycle cost savings and 15% service life extension, underscoring its potential for data-driven pavement management.