The difficulties of engineers and managers agreeing on how to invest in infrastructure maintenance stem from a basic inability to communicate with each other. This leads to the suboptimal management of infrastructure. Luckily, this situation can be remedied by engineers learning how to communicate their concerns in a way that managers can understand, so that they can clearly see whether a proposed action needs to be taken or can be deferred. This paper shows, with the help of a realistic example, in terms of infrastructure, methodology and techniques, how this can be done. The proposed approach, although upon reading is perhaps intuitive, is starkly absent in the literature in the field of infrastructure asset management. In the proposed approach, it is demonstrated how to improve the traditional approaches used by engineers to communicate to managers through (a) the quantification of the level of service as seen by mangers, (b) the modelling of how infrastructure might not provide the required level of service and (c) the way of showing how intervention programmes can affect the provision of service, both now and in the future.

Railway networks are constructed, maintained and developed to provide service to stakeholders. The timing of infrastructure interventions depends on the costs of intervening and the risks of not intervening. Determining the optimal trade-off requires a rigorous definition of the service provided and a way to quantify it. In this paper, such a definition and method of service quantification for the railway infrastructure is provided. Service is defined as a function of how every stakeholder is affected by the changes in the state of the railway infrastructure. It is quantified by estimating the value of each unit of service provided. This definition of service enables the quantification of railway service for all the affected stakeholders when the infrastructure state improves – for example, due to maintenance – or deteriorates – for example, due to fatigue.

To ensure that transport infrastructure provides acceptable levels of service with respect to extreme events, the resilience of the infrastructure needs to be estimated and targets for it need to be set. In this paper, the methodology proposed in the Foresee EU research project is presented. The methodology allows managers to measure, and set targets for, the resilience of transport systems in all situations. It requires clear definition of the transport system and how the service provided and the resilience are to be measured. The methodology allows consideration of the fact that transport infrastructure managers need to estimate resilience with various degrees of accuracy depending on the specific problem to be addressed, the time frame at disposition and the expertise available. These various levels of accuracy are covered by proposing the use of (a) simulations, (b) indicators whose values are directly related to increases in expected restoration intervention costs and reductions in service, and (c) the percentage of fulfilment of indicators. Once resilience has been estimated, the methodology provides guidance on how to set resilience targets with or without cost–benefit analysis. For demonstration, the explanation of the steps of the guideline is supported by their use for a simple transport system.

Railways are critical infrastructure systems because their construction, use, operation, maintenance and development affect multiple stakeholders within society. To ensure that these systems function as intended over time, railway managers execute interventions. Railway managers are interested in determining which interventions to execute by considering the trade-offs between the intervention costs and effects on service consistently and transparently. Currently, however, this is not possible, because existing methodologies use qualitative indicators instead of quantifying the rail service directly. This paper shows how the direct quantification of the effects on rail service per unit time enables interventions to be selected by making optimal trade-offs between intervention costs and effects on service when the acceptable service levels are respected. The usefulness of direct quantification is demonstrated by comparing two sets of intervention strategies for the track, switches and bridges of an example network for 30 years, by using direct estimates of service and four service indicators, state, reliability, availability and safety. As railway management moves from qualitative to quantitative and data-driven decision-making, the direct quantification of the effects on service provides a more informative way to compare intervention strategies than the use of service indicators.

Railway managers plan interventions on infrastructures to reduce risks. To plan these interventions effectively, they need comparable risk estimates for all the assets, by considering all types of failure causes and failure modes and all the costs and effects on stakeholders due to these failures. However, existing risk models have been developed for specific asset types and specific failure scenarios, and they result in a wide range of qualitative and quantitative results that are not compatible. Hence, often managers cannot use the results of these models directly in an aggregated assessment to prioritise risk-reducing interventions across a network effectively. This paper presents a methodology for using results from existing models in a systematic way to produce comparable risk estimates for different asset types. Although the methodology is based on simple standard tools, it is the first time that they have been brought together in a way that will help infrastructure managers make consistent risk estimates by systematically determining scenarios related to asset failures and estimating their effects on the railway service in comparable units. The usefulness of the methodology is demonstrated by using it to produce comparable risk estimates for the track, switches and bridges of a part of an Irish network.

Railway managers identify and prioritize assets for risk-reducing interventions. This requires the estimation of risks due to failures, as well as the estimation of costs and effects due to interventions. This, in turn, requires the estimation of values of numerous input variables. As there is uncertainty related to the initial input estimates, there is uncertainty in the output, i.e., assets to be prioritized for risk-reducing interventions. Consequently, managers are confronted with two questions: Do the uncertainties in inputs cause significant uncertainty in the output? If so, where should efforts be concentrated to quantify them? This paper discusses the identification of input uncertainties that are likely to affect railway asset prioritization for risk-reducing interventions. Once the track sections, switches and bridges of a part of the Irish railway network were prioritized using best estimates of inputs, they were again prioritized using: (1) reasonably low and high estimates, and (2) Monte Carlo sampling from skewed normal distributions, where the low and high estimates encompass the 95% confidence interval. The results show that only uncertainty in a few inputs influences the prioritization of the assets for risk-reducing interventions. Reliable prioritization of assets can be achieved by quantifying the uncertainties in these particular inputs.