Horizon 2020 Marie Skłodowska-Curie Innovative Training Network


Railway bridge condition monitoring and fault diagnostics
ESR9 2017-05-31T22:26:44+00:00
ESR9: Railway bridge condition monitoring and fault diagnostics
The state of bridge structures is usually determined by manual inspection carried out at fixed intervals of time. When deterioration is observed at a degree which requires attention the work is scheduled for action. The process has many flaws including the fact that parts of the structure (such as the cables in suspension bridges) cannot be observed.

On some bridges, sensors are located to monitor deflections and/or vibration to assist in determining the bridge condition but these are not integrated with fault diagnostic approaches which not only indicate when the structure needs attention but the location of the problem elements.

By developing an ability to determine remotely when a bridge needs attention and in addition what that requirement will be, TRUSS will vastly reduce the whole life cost of the asset. It will also ensure that the structure is in a safe condition to operate. When accounting for all bridges in the world, many of which are in a poor condition, this approach has the potential to save substantial funding currently expended on this process.

This project is to design a method by which a fault diagnostic process can be established for bridges and will initially fix one particular bridge type determined by UNOTT jointly with URS. The design approach will determine the methodology and the number of sensors installed. Metal underbridges make up a large proportion of the bridges used on European railway systems. As their state deteriorates, by mechanisms such as corrosion, the strength of the structure reduces and the deflections it experiences with passing load increases. The state of the track used on the bridge is established by a measurement train at regular intervals. This has the effect of introducing a known load to the structure.

Displacement patterns can be predicted for the bridge as the load moves across it using Finite Element (FE) models for a variety of deteriorated states of the bridge members. By using sensors to track the deflection patterns over time the deteriorated state of the structure can be monitored.

The aim would be to use fault diagnostic methods such as Bayesian Belief Networks and pattern recognition methods such as Neural Networks to relate the measurements from the bridge to the state of the different members. In this way not only can the condition of the bridge be monitored but, when this falls below the required performance, the potential causes of the problem, where maintenance is to be focused, can be established.
Improved fault-diagnostic and pattern recognition monitoring methods of bridge safety based on deflection patterns.
This project involves a secondment of some months to URS (supervised by Dr. Matthew Brough). Of particular relevance to this project is the structures and asset management team in URS. They are highly experienced in investigation, survey, monitoring, design and modelling all types of highway and railway footbridges and viaducts.

They will provide the ESR info for a particular structure and assistance in its modelling, for use in developing the state diagnostic methodology. The ESR will gather information about the structure design and the states of the elements and start to generate a FE model of the structure.

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