The objective of this study is to show the applicability of machine learning and simulative approaches to the development of decision support systems for railway asset management. These techniques are applied within the generic framework developed and tested within the In2Smart project. The framework is composed by different building blocks, in order to show the complete process from data collection and knowledge extraction to the real-world decisions. The application of the framework to two different real-world case studies is described: the first case study deals with strategic earthworks asset management, while the second case study considers the tactical and operational planning of track circuits’ maintenance. Although different methodologies are applied and different planning levels are considered, both the case studies follow the same general framework, demonstrating the generality of the approach. The potentiality of combining machine learning techniques with simulative approaches to replicate real processes is shown, evaluating the key performance indicators employed within the considered asset management process. Finally, the results of the validation are reported as well as the developed human–machine interfaces for output visualization.

This paper presents a novel approach for anomaly detection on the Settefonti Bridge by combining advanced operational modal analysis (OMA) technique with artificial intelligence. Ambient vibration data from accelerometers is processed using the covariance‐driven stochastic subspace identification (CoV‐SSI) method to extract modal parameters, which are then analyzed using statistical pattern recognition and Hotelling’s T2 control charts. In parallel, strain gauge time series data are evaluated with deep neural network‐based autoencoders that detect anomalies via reconstruction error analysis. This integrated framework enables robust, continuous structural monitoring and facilitates early damage detection, thereby supporting timely maintenance and enhanced safety of the bridge.

CEMBOX Digital Twin oriented Platform has been conceived to address the pressing needs in terms of design, maintenance, retrofitting and digitisation in the fields of infrastructure and mobility. The Platform’s Digital Twin is conceived as a real-time replica of the infrastructure that enables (i) the visualisation of assets in an interoperable BIM-GIS environment, (ii) the inspection of the state of infrastructure assets, integrating advanced inspection and monitoring technologies by means of IoT systems and other ICT enablers, and (iii) infrastructure condition analysis by means of Artificial Intelligence and advanced structural health monitoring algorithms, analysing the evolution of condition state over time, so as to support asset management decision-making. Following the needs of infrastructure asset inspection, maintenance, and management, CEMBOX has been orchestrated into four main services described in this article: Management, Inspection, Monitoring and Planning.

1. Water-resources management is increasingly challenged by climate variability, growing water demand and the need to balance environmental, social and economic objectives at the basin scale (Monta...

This paper presents a methodology for assessing the sustainability of pavements through indicators. There are increasingly different procedures, technologies and practices to increase the sustainability of construction works. Especially in the field of roads, research work is being carried out to develop new asphalt mixes with low emissions, as well as to promote the reuse of recycled materials. All of these practices will greatly contribute to sustainability. However, sustainability is a relative parameter that should be used for comparison. There are no sustainable activities in absolute terms, but there are activities that are more or less sustainable compared to others. When the sustainability of two different products or activities is evaluated, the aim is to compare them and, therefore, the evaluation must be carried out following a homogeneous procedure. This paper introduces a methodology based on KPIs that will provide pavement designers with a tool to quantify and compare the sustainability of different pavement structures in order to select the best pavement alternative from a technical, economic and sustainable point of view.

The OMICRON project develops an Intelligent Road Asset Management Platform integrating a broad portfolio of area specific innovative technologies to enhance the construction, maintenance, renewal and upgrade of the European road network. The project improves the whole asset management pipeline focusing on four pillars: modular construction of bridges, road inspection digitalisation, predictive maintenance and smart execution of intervention actions. OMICRON’s Intelligent Platform enables the digitalisation and automation of a relevant portfolio of road management tasks which are still very labour intensive. Thereby, OMICRON aims to pave the way to the roads of the future, addressing the reduction of fatal accidents related to maintenance actions, the reduction of traffic disruptions, the reduction of maintenance costs, and the increase in road network capacity.