Conceptual Investigation of a Digital Twin-Based Framework for Proactive Road Surface Maintenance: A Preliminary Study

Introduction: Cost-effective and sustainable pavement management increasingly relies on proactive maintenance strategies supported by predictive and data-driven tools. Digital Twin (DT) technology has recently emerged as a promising approach for continuous pavement condition monitoring and maintenance decision support.

Methods: This preliminary study develops a conceptual DT-based framework for road surface maintenance. The framework architecture was structured through a review of pavement monitoring requirements, identification of core DT components, and definition of data flows between field measurements, historical records, and maintenance decision processes. Historical International Roughness Index (IRI) records from the Long-Term Pavement Performance (LTPP) database were used as an illustrative data source to demonstrate how roughness information could be integrated into a DT environment.

Results: The study proposes a conceptual architecture that links historical IRI records, condition monitoring, trend interpretation, and maintenance planning. The framework highlights the potential role of roughness evolution in supporting early identification of pavement deterioration and in distinguishing surface-related degradation from possible underlying structural issues. In addition, the study identifies key methodological requirements for future implementation, including data continuity, monitoring frequency, and integration of additional pavement condition indicators.

Conclusions: The work does not present a fully implemented DT system; rather, it provides a structured conceptual basis for future development of DT-enabled pavement management tools. The proposed framework contributes to the ongoing discussion on digitalization in road asset management and outlines research directions toward predictive and proactive road maintenance strategies.