A risk-informed decision support tool for the strategic asset management of railway track infrastructure

Research output: Contribution to journalArticlepeer-review

Abstract

The provision of safe, efficient, reliable and affordable railway transport requires the railway track infrastructure to be maintained to an appropriate condition. Given the constrained budgets under which the infrastructure is managed, maintenance needs to be predicted in advance of track failure, prioritized and identified risks and uncertainties need to be considered within the decision-making process. This paper describes a risk-informed approach that can be used to economically justify railway track infrastructure condition by comparing on a life-cycle basis infrastructure maintenance costs, train operating costs, travel time costs, safety, social and environmental impacts. The approach represents a step-change for the railway industry as it will enable economic maintenance standards to be derived which considers the needs of the infrastructure operator, but also those of users, train operating companies and the environment. Further, the risk-informed capability of the tool enables asset managers to deal with uncertainties associated with forecasting costs and the effects of track maintenance, and unavailability of data. The Monte Carlo simulation technique and a Fuzzy reasoning approach are used to address safety data uncertainties through probabilistic risk assessment allied to expert opinion. The approach is illustrated using data from three routes on the UK mainline railway network. The results demonstrate that the approach can be used to support strategic and tactical levels of railway asset management to inform plausible design and maintenance strategies that realise the maximum benefit for the available budget.

Details

Original languageEnglish
JournalProceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
Early online date5 Aug 2021
Publication statusE-pub ahead of print - 5 Aug 2021

Keywords

  • Derailment, uncertainty, safety, expert judgement, risk assessment, fuzzy, Monte Carlo, track quality, information, decision making, asset management