Leveraging Digital Twin Technology for Data-Driven Pavement Maintenance

Abstract

Integrating Digital Twin (DT) technology with data from automated pavement data collection resources, such as Autonomous Vehicles (AVs), offers a revolutionary approach to proactive pavement maintenance planning. This article proposes a comprehensive framework that utilizes diverse data sources, including AVs, sensors, automated data collection vehicles, and maintenance vehicles, to provide precise, real-time pavement condition data for better-informed maintenance decisions. Building Information Modeling (BIM) is used to create a digital representation of the pavement, facilitating visualization and simulation, leading to cognitive DT. Advanced AI analytics are utilized to detect pavement distress, optimize maintenance planning, and predict deterioration. The framework's strength is demonstrated through a case study on a Finnish motorway, highlighting potential improvements in maintenance efficiency, reduced reactive repair costs, and enhanced road safety. This research highlights the benefits of DT technology in pavement maintenance, including improved performance, longevity, and sustainability of road infrastructures, paving the way for wider adoption by road agencies.