Aged earthworks constitute a major proportion of European rail infrastructures, the replacement and remediation of which poses a serious problem. Considering the scale of the networks involved, it is infeasible both in terms of track downtime and money to replace all of these assets. It is, therefore, imperative to develop a rational means of managing slope infrastructure to determine the best use of available resources and plan maintenance in order of criticality. To do so, it is necessary to not just consider the structural performance of the asset but also to consider the safety and security of its users, the socioeconomic impact of remediation/failure and the relative importance of the asset to the network. This paper addresses this by looking at maintenance planning on a network level using multi-attribute utility theory (MAUT). MAUT is a methodology that allows one to balance the priorities of different objectives in a harmonious fashion allowing for a holistic means of ranking assets and, subsequently, a rational means of investing in maintenance. In this situation, three different attributes are considered when examining the utility of different maintenance options, namely availability (the user cost), economy (the financial implications) and structural reliability (the structural performance and subsequent safety of the structure). The main impact of this paper is to showcase that network maintenance planning can be carried out proactively in a manner that is balanced against the needs of the organization.

Ultra-high-performance fiber-reinforced cement-based composite (UHPFRC) has been increasingly adopted for rehabilitation projects over the past two decades, proving itself as a reliable, cost-efficient and sustainable alternative against conventional methods. High compressive strength, low permeability and high ductility are some of the characteristics that render UHPFRC an excellent material for repairing existing aged infrastructure. UHPFRC is most commonly applied as a surface layer for strengthening and rehabilitating concrete structures such as bridge decks or building slabs. However, its implementation with steel structures has so far been limited. In this work, the UHPFRC strengthening of a steel bridge is investigated both in simulation as well as in the laboratory, by exploiting a real-world case study: the Buna Bridge. This Croatian riveted steel bridge, constructed in 1893, repaired in 1953, and decommissioned since 2010, was removed from its original location and transported to laboratory facilities for testing prior to and after rehabilitation via addition of UHPFRC slab. The testing campaign includes static and dynamic experiments featuring state-of-the-art monitoring systems such as embedded fiber optics, acoustic emission sensors and digital image correlation. The information obtained prior to rehabilitation serves for characterization of the actual condition of the structure and allows the design of the rehabilitation solution. The UHPFRC slab thickness was optimized to deliver optimal fatigue and ultimate capacity improvement at reasonable cost. Once the design was implemented, a second round of experiments was conducted in order to confirm the validity of the solution, with particular attention allocated to the interface between the steel substrate and the UHPFRC overlay, as the connection between both materials may result in a weak contact point. A detailed fatigue analysis, based on updated FEM models prior to and after strengthening, combined with the results of a reliability analysis prove the benefits of adoption of such a solution via the significant extension of the structural lifespan.

In recent times, there has been an increase in transport infrastructure failure. This increase is due to aging infrastructure, increased number of extreme weather events caused by climate change, and increased traffic loading. Accordingly, the need for planned and unplanned maintenance interventions is rising. Associated costs do not only involve direct maintenance or reconstruction costs, but also secondary effects experienced by users of the transport network as well as the environment and society in general. Infrastructure managers require tools for accurate quantification of infrastructure resilience that will enable rational adaptation investment strategies, so as to maintain high level of safety of transport networks. Through the development of a Global Safety Framework, at the core of which is a Multi-modal Network Decision Support Tool, the SAFE-10-T project (Safety of Transport Infrastructure on the TEN-T Network) is providing integrated solutions to issues related to infrastructure safety and planning. The paper presents a reliability-based whole life cycle model developed within this project enabling strategic investment decisions that maximize safety, minimize disruption, and environmental impacts and allow for the best use of limited resources. The model is applied on a case study of a bridge in the Port of Rotterdam in the Netherlands.

This paper presents an end-to-end framework for automated inspection and condition assessment of airport runway pavement using UAV-acquired imagery. The proposed approach integrates Unmanned Aerial Vehicle (UAV)-based data collection, deep learning-based pixel-level semantic segmentation of surface defects, and Geographic Information System (GIS)-based spatial aggregation to generate a georeferenced digital representation of airfield pavement condition. Multiple safety-critical defect types are detected and localized at pixel resolution, while spatially referenced processing enables a Pavement Condition Index (PCI)-inspired condition assessment based on defect density within predefined sampling units. The framework is validated through a real-world case study at Zadar Airport, where the entire runway was surveyed using high-resolution UAV imagery. The results demonstrate the system’s capability to identify and map multiple defect categories across the full runway extent and to produce a coherent, runway-scale condition map supporting maintenance prioritization and decision-making. Overall, the proposed solution provides a scalable, data-driven alternative to traditional manual runway inspection workflows and establishes a practical foundation for digital condition monitoring of airport pavement infrastructure.

Road maintenance remains one of the most resource-intensive and hazardous operations in infrastructure management. Traditional inspection practices rely heavily on manual labour and discrete procedures, often resulting in limited scalability, operator exposure to traffic hazards, and inefficiencies in data collection. This paper presents a novel automated methodology that integrates Unmanned Aerial Vehicles (UAVs) and autonomous mobile robots (AMRs) to enable automated inspection and measurement of road assets through a digital twin (DT) system. The system leverages data fusion and real-time synchronisation between field agents and a centralised digital twin to monitor the retro-reflectivity of vertical and horizontal signage, detect obstacles and vegetation, and support data-driven maintenance planning. A case study conducted on the Italian highway network demonstrated improvements in operational safety, inspection efficiency, and measurement consistency. The results confirm that the integration of UAVs and AMRs within a digital twin framework can significantly improve sustainability, productivity, and workers’ safety in road maintenance operations.