Inspection and maintenance activities for transport infrastructure are substantial contributors to global carbon emissions. Traditionally, maintenance strategies have been divided into corrective and preventive approaches. Today, the transport sector is witnessing a marked transition from corrective to proactive maintenance, especially predictive maintenance, aimed at reducing costs, optimizing intervention schedules, lowering carbon footprints, and minimizing disruption across transport modes.

The advent of Artificial Intelligence (AI), including Machine Learning (ML) and Deep Learning (DL), is catalyzing this transformation. Through the integration of multimodal data from infrastructure health monitoring and advanced digital modeling, transport infrastructure managers can now implement predictive maintenance strategies that substantially improve service efficiency and sustainability.
This Special Issue (SI) aims to advance the integration and adoption of Predictive Maintenance (PdM) and eXplainable Predictive Maintenance (XPdM) across the full lifecycle of large-scale transport infrastructure, including railways, metro systems, roads, and airfields. We seek to highlight pioneering research that leverages digitalization, sensor technologies, and AI-driven models to transform structural health monitoring (SHM) and maintenance in the transport sector. Special emphasis is placed on innovative approaches that promote sustainability, circularity, and operational efficiency within maintenance practices.

Scope & Topics

We invite original research articles, reviews, case studies, and technical notes addressing (but not limited to) the following themes:

o Digital twins for development of PdM and XPdM within transport infrastructure
o Application of Building Information Modelling (BIM) and Geographic Information Systems (GIS) for infrastructure health forecasting
o Sustainable and circular maintenance strategy development within the transport sector
o Integration of AI and sensor-based Internet of Things (IoT) systems for intelligent monitoring of transport assets
o Use of ML and DL methods for predicting failure and estimating remaining useful life of transport infrastructure components
o Harnessing explainable AI within decision-support frameworks for predictive maintenance
o Capturing and analyzing resilience in maintenance activities across various transport modes
o Human-centred and ethical AI for participatory predictive maintenance systems
o Alignment of PdM and XPdM with Industry 5.0 principles

Submission Formats

We welcome the following types of contributions:
o Original Research Articles
o Review Articles
o Case Studies / Field Studies
o Short Communications / Technical Notes

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Digital transformation in Structural Health Monitoring of transport infrastructure