The Economic Service Life of Track

The Economic Service Life of Track
Analysing life-cycle-cost of track depicts (re-)investment causing more than 50% of total track cost. Thus the identification of the economic service life is of major importance for reducing track costs.
In principle the economic service life is reached as soon as the increase of maintenance costs overshoots the decrease of depreciation. Thus forecasting the maintenance demand is a precondition for service life optimisation.
This forecast must be valid for specific track sections, as track reacts very sensitive and differently due to its varying boundary conditions. Thus the maintenance prediction is based on specific data of the investigated track section.
The Institute of Railway Engineering and Transport Economy (TUGraz) has a long lasting and close cooperation with the Austrian Federal Railways (OeBB). Based on that TUGraz built up a data-warehouse containing all track relevant data; infrastructure data as alignment, recording car data, type and age of track, sub-layer and sub-soil information, executed maintenance, and operational data as transport volume, and transport speed. These data are precisely positioned and put into time sequences covering already 14 years.
Based on these time sequences trends of degradation of entire track as well as component behaviour can be analysed and used for predicting the maintenance demand. Furthermore, these track work specific predictions need to be adjusted, due to the interrelations of different maintenance actions.
However, these prognoses deliver the demand of track maintenance, but no information about the economic service life itself. It can be identified by economic evaluations, named annuity monitoring. The average annual costs are calculated due to the maintenance schedule from the starting point of track life. Increasing service life reduces depreciation and increases maintenance costs and its consecutive costs of train operation as delays, route deviation or rail replacement transports. Taking all these costs into account a minimum of total track cost can be calculated. This cost minimum is defining the economic service life. If this service life is exceeded track becomes more expensive. As the economic calculation is not finished at that point of time the cost increase in postponing a re-investment project is a further result of the calculation. In case of limited budgets these results allow ranking of track re-investment due to the additional costs of postponing it.
As this methodology is already used as a pilot for identifying the optimal point in time of track re-investment projects of OeBB, the preciseness of the methodology and thus the maintenance predictions is already verified for a time span well above the period needed for project planning