The continuous monitoring of long-term performance of tunnels constructed in soft rock masses shows that the rock mass deformations continue after construction, albeit at a rate that reduces with time. This is in contrast with NATM postulates which assume deformation stabilizes shortly after tunnel construction. This paper proposes the prediction of long-term vertical settlement performance of a tunnel in soft rock mass, through the inclusion of a Burger’s creep viscous-plastic constitutive law to model post-construction deformations. To overcome issues related to the complex characterization of this constitutive model, a neural network NetRHEO is developed and trained on a numerically obtained dataset. A particle swarm algorithm is then employed to estimate the most probable rheological parameter set, by utilizing the long-term in-situ monitoring data from several observation points on a real tunnel. The paper demonstrates the potential of the proposed methodology, using displacement measurements of two adjacent tunnels in karstic rock mass in Croatia. The complex interaction of a railway tunnel Brajdica and a road tunnel Pećine, conditioned by the character of the surrounding rock mass as well by the chronology of their construction, was evaluated to predict the future behavior of these tunnels.@en

Aged earthworks constitute a major proportion of European rail infrastructures, the re-placement 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.

@en

In the current economic climate, it is crucial to optimize the use of all resources regarding railway infrastructure maintenance. In this paper, a multi-attribute decision support framework is applied to categorize railway embankments in order to prioritize maintenance activities. The paper describes a methodology to first determine the current condition of embankments using a combination of ground penetrating radar (GPR) surveys, visual inspection, and historical data about maintenance activities. These attributes are then used for the development of a multi-attribute utility theory model, which can be used as a support for decision making process for maintenance planning. The methodology is demonstrated for the categorization of 181 km of railway embankments in Croatia.

@en

There have been significant developments in the area of vibration-based bridge scour monitoring in recent years. Traditional scour monitoring using either visual assessment or diving inspections are now recognised to be very unreliable and highly subjective. There has been a concerted effort to move towards reliable systems capable of either direct measurement of scour or indirect measurement, based on monitoring the response of the structure to damage. The developments have unearthed new challenges and problems. This paper describes some recent developments in the field. In addition, remaining challenges that act as a barrier to the successful wide-scale deployment of the methodologies are discussed. In particular, it addresses issues related to how to measure key performance indicators (such as the vibration response of the structure) and the potential of these approaches in real-world applications.@en

Infrastructure maintenance is a complex task due to operational needs of service quality, availability demands, traffic intensity, and budget constraints. Traditionally, maintenance decisions are based on infrastructure managers' experiences, judgments, and available choices. Though, the technology push and the availability of an abundance of data have made an urge to derive the decisions and insights from data. This data-driven approach resulted in computerized solutions, e.g. decision support system (DSS), which are rich in data but poor in insights. The DSS confronted with data management challenges of data acquisitions, data cleaning, and data reliability further complicate the already complex task of maintenance decision-making. To tackle these challenges of maintenance decision-making and data management, a decision-driven approach is suggested for the development of DSS. The decision-driven approach builds on the definition of decision context specified by infrastructure manager. The data requirements are provided by decision context, where the interrelationships between the data and the decision context are made explicit by developing an information model. We illustrate the decision-driven approach for DSS development using a case study of maintenance decisions for bridge selection. It is found that the decision-driven approach directs the focus towards the decision context definition and decision analysis while minimizing the overhead of data management.@en

Since more than 95% of the existing European railway network is older than 100 years, the ageing has unavoidably caused its gradual degradation and there is currently a strong need for risk assessment associated with this type of infrastructure. To increase the safety aspects, as well as to reduce the cost of remediation, railway infrastructure managers need to have more advanced tools on their disposal, since they currently make safety critical investment decisions based on poor data and an over-reliance on visual assessment. As a consequence their estimates of risk are therefore highly questionable and largescale failures happen with increasing regularity. The paper presents the efforts conducted within the framework of some relevant scientific-research projects funded by the Horizon 2020 programme, whose overall goal is to implement and to further develop state-of-the-art techniques and tools in field of railway infrastructure safety.@en