Modeling heterogeneous spatiotemporal pavement data for condition prediction and preventive maintenance in digital twin-enabled highway management

Pavement preventive maintenance is one of the most fundamental use cases when deploying digital twins (DTs) for highway infrastructure management. To achieve this, it is essential to accurately predict the pavement conditions in future years. This paper developed a Spatial-Temporal Graph Attention network (STGAT) that can effectively capitalize on both spatial and temporal dependencies while addressing inherent heterogeneity in pavement data for more accurate condition predictions. On top of this, a structured assessment procedure was introduced to determine the need for preventive maintenance on road sections based on the STGAT predictions. A case study on the highway network in the United Kingdom was conducted to evaluate the method's performance. The results showed that the proposed method can achieve superior accuracy for pavement condition prediction and subsequent preventive maintenance assessment compared to existing methods, thus signifying its potential to improve the effectiveness of DTs for highway infrastructure management. • Digital twins (DTs) are a promising technology for highway infrastructure management. • A Spatial-Temporal Graph Attention network (STGAT) was developed for accurate pavement condition prediction. • The developed STGAT model can capitalize on heterogeneous spatial-temporal pavement data. • An assessment procedure was introduced to identify road sections requiring preventive maintenance. • The method's performance was evaluated through a case study on the highway network in the United Kingdom.