Xiang Xu

Big data and data-centric engineering – the Forth Bridges

The Queensferry Crossing is a three-tower cable-stayed bridge with a main span of 650m. The Queensferry Crossing opened to traffic in September 2017 and is equipped with 1972 sensors which continuously monitor the bridge.

The Forth Road Bridge is a suspension bridge with a main span of 1006m, which opened in 1964 and at that time was the largest suspension bridge in the world outside the USA. The Forth Road Bridge is equipped with 180 sensors which continuously monitor the bridge.

The project aims to develop a new holistic Predictive Maintenance System leveraging the data collected by the SHM system, during visual inspections and through portable NDT devices (i.e. Proceq tools). Big data analysis and machine learning algorithms applied on that collected data will enable developing generic software tools for the predictive maintenance of the Forth Bridges. These generic software tools should then be applicable to other similar structures, however requiring some retraining to capture the different environmental, service, loading conditions, and more.

The specific objectives are:

  • Develop techniques for “Big Data Analysis” from the 1972 sensors on the Queensferry Crossing, data collected from visual inspections as well as portable NDT devices using machine learning techniques;
  • Developing an FE model of the Queensferry Crossing as a precursor towards developing a ‘Digital Twin’ model of the bridge;
  • Combining the Data Analysis with the Digital Twin will yield a data-centric engineering solution;
  • Develop concept of the holistic variable (Hv) that shall be used to indicate the ideal moment to activate maintenance works on the structure;
  • Develop holistic predictive maintenance models that shall lately be used to estimate the ideal moment to activate maintenance works on the structure.
  • The analysis tools developed and used on the Queensferry Crossing will be used on the Forth Road Bridge (a suspension bridge, now in urgent need of essential repair work to extend its operational life).


This TRAIN@Ed project has received funding from the DDI programme, the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 801215, and industrial funding from Screening Eagle Technologies.

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Xiang Xu

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