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)