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Robust infrastructure – Adapting railway maintenance to climate change (CliMaint)

CliMaint Summary: Increased intensity and frequency of extreme weather conditions caused by climate change have a negative impact on rail service performance and related costs. Research has shown that adverse weather conditions are responsible for 5 to 10% of total failures and 60% of delays on the railway infrastructure in northern Europe.

In Sweden, about 50% of train delays are caused by weather related failures in switches and crossings, where winter maintenance of switches and crossings cost on average of 300MSEK annually. These costs are expected to increase due to intensity and frequency of extreme weather events. It is critical for infrastructure manager to predict “What will be the required maintenance and renewal policies to ensure robust and resilient infrastructure to deals with climate changes for short- and long-term scenarios?” and “How maintenance cost will increase due to climate changes?”CliMaint addresses the following targets:
- Better understanding of the mechanism and causal relationship between climate parameters and infrastructure conditions,
- Development of theoretical models for integrating meteorological data into the maintenance process of the railway,
- Implementing and planning of new maintenance solutions for transport infrastructure in order to improve reliability and cost-effectiveness of future investments.
The methodology implemented in CliMaint can have significant impacts of 5-10% reduction in the traffic disruptions and maintenance costs.
The aim of CliMaint is to ensure robust and reliable railway infrastructure by maintenance adaptation to climate change. The objective of the project is to reduce future disturbances due to extreme climate conditions by effective maintenance program. The objective will be achieved by utilizing RAMS (reliability, availability, maintainability and safety) methodology and integrating infrastructure degradation modelling with metrological and satellite information.

WP1: Project management and dissemination

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WP2: Data collection and pre-processing

This WP will investigate potential impacts of climate change on the safety and performance of railway infrastructure by literature review (SoA). Accordingly, data is collected in relation to maintenance (inspection and repair, renewal data, maintenance report) for railway infrastructure satellite and metrological data for region. The plan is to study the failure data of railway infrastructure since 2000 in Norbotten will be investigated. In addition, Interview and questionnaire will be distributed to the experts and society.

WP3: Climate change and extreme weather events for railway

In this WP, spatio-temporal variation in permafrost and snow cover in association with changes in land surface temperature will be analysed. In addition, analysis of frequency and intensity of extreme weather events will also be carried out.Several climate scenarios from global climate models, downscaled first to the regional scale and further downscaled to the rail segments scale which provide a robust information of future climate for a short- and long-term period. Change of indices in the future should then be used in assessments of how the vulnerability of the railway system changes in the future.

Two scenarios can be designed as short and long term for instance, 2020-2049 and 2071-2100.

WP4: State assessment of railway infrastructure

WP4 deals with historical operation and maintenance data to identify failure modes and degradation which potentially cause by extreme weather condition from WP2. Analyze failure modes and their associated frequencies including observable or measurable signs that point to these modes and to potential existence of deterioration mechanisms. In addition, the predictive models developed from the previous projects on railway infrastructure will be incorporated for instance, EU projects.

WP5: Integrated Climate Predictive Maintenance Analytics

The work package five will focus on the prediction of failure, associated maintenance cost and risk due to climate changes in the short- and long-term scenario. The prediction model will be developed by inputs from satellite observations and metrological forecast model from WP3 and asset condition model developed in WP4.

Partners: Trafikverket, Transportstyrelsen, InfraNord, JVTC, SMHI, LTU, Luleå kommun, Vinnova och Sweco.