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Assessment of the civil engineering infrastructure of railway networks such as bridges, tunnels and slopes is traditionally performed by defining a model which includes the problem geometry and material properties and loading conditions. Partial safety factors are applied to the loads and material properties (i.e. loads are increased and resistance is decreased) and a snap-shot estimate of the safety of the structure in its current state is obtained. The advantage of the traditional approach is that it is easy and convenient to use. However, the disadvantage of using this approach for the assessment of existing infrastructure is that, because of adopting unduly conservative safety factors, the capacity and ultimately the remaining safe life of the structure can be underestimated.

In bridge structures, Structural Health Monitoring (SHM) techniques for assessing the performance of a new structure during construction or in its as-built state and the assessment of performance of any rehabilitation or the evolution of safety of a degrading structure are available. The sensor data are analysed to develop SHM markers. The markers typically seek to detect varied events, sudden events and the evolution of the performance of the structure with time.  Typically, they include identification of events in the form of some kind of an outlier and the characterisation and calibration of a certain time-span in the form of a defined domain of values. Wavelet based analyses are gaining significant popularity in this regard in the scientific literature. In terms of approach, statistical techniques have also been shown to have great promise. There is some limited existing work based on experiments of full scale bridges. A number of them consider wavelet based techniques while the use of various monitoring devices have also been investigated. Data dependent numerical and statistical algorithms have been observed to be very successful in identifying events. These experiments are extremely important since they not only provide a high degree of confidence regarding the use of a method but also delineate the practical limitations and problems associated with such detection.

To date sensor information has mostly been employed in addressing specific problems in bridge monitoring, maintenance and management. The direct translation of such data is not available for a general condition rating. Some research projects have started to link bridge ratings with sensor data. In principle, such a correlation or a ranking has been seen to be of great importance although work carried out in this regard has been limited to visual inspections. It is concluded that the development of algorithms to link continuously observed sensor data with the rating of a bridge would be of significant benefit. The condition rating derived in this process would be significantly more robust, than those currently available for non-instrumented bridges, due to the reduction in both epistemic and aleatory uncertainties. 

A sizeable portion of maintenance budgets for the rail networks considered in this research is spent on maintenance and renewal of the ballast in the track-bed. The primary reason for this maintenance is the fact that a railway track structure is not designed to retain its shape for long periods. Differential permanent deformation (i.e. settlement) develops quickly under traffic loading causing a reduction in riding quality which, if not corrected, compromises safety. The principal material in which the deformation takes place is the ballast, which consists of approximately single-sized aggregate, meeting specified criteria for size, shape and durability but not for overall mechanical properties. In relative terms, it is a cheap material and it has the advantage that it can be ‘reshaped’ when necessary, enabling correction of track line and level to be carried out. This reshaping is most commonly conducted using a tamping machine, which physically lifts the track back to the correct position, inserts steel tines into the ballast and packs ballast stones under the lifted sleepers. This is not a particularly difficult operation, nor is it very expensive (in terms of €/km), but it is highly disruptive to operations and also contributes significantly to the process of ballast degradation, which is much more expensive to correct.

  • Probability based approaches
  • Use of sensor data to analyse current state
  • Reliability of slopes
  • Track-settlement and stiffness
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