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Centre for Digital Built Britain

 

A major rail upgrade programme to remove a bottleneck on the West Coast Main Line saw collaboration between Atkins, Laing O’Rourke, Volker Rail and Network Rail. The Stafford Area Improvement Programme included construction of a number of new bridges designed by Atkins using Building Information Modelling (BIM) from the outset and off-site construction techniques wherever possible. The Staffordshire Alliance team handed over the £250m upgrade in 2015, including 10 kilometres of new line, under budget and more than a year ahead of schedule [1].

Researchers at the Centre for Smart Infrastructure and Construction (CSIC) and the Laing O’Rourke Centre for Construction Engineering and Technology (LORC) were embedded in the project from the start to instrument two of the new bridges with fibre optic sensors to better understand their structural behaviour from the beginning of their service life.

The monitoring systems on the bridges generate vast amounts of data and CSIC/LORC researchers are currently collaborating with The Alan Turing Institute to develop methods to analyse the rich data sets. This project, working with Network Rail, will demonstrate the use of real-time data analytics integrated with digital twins to provide useful information to support engineers and asset managers to schedule safer and proactive maintenance programmes and optimise future designs.

Big picture

Bridges are critical structures that connect communities and form a significant part of our national infrastructure network and associated services. Many bridges are ageing and nearing the end of their designed service life while carrying significantly more and heavier vehicles than originally expected. In addition, bridges are exposed to harsher environmental conditions due to the effects of climate change which can lead to further deterioration. Maintenance and repairs can be costly: according to an annual survey (2020) by the RAC Foundation and ADEPT (the Association of Directors of Environment, Planning and Transport)[2] the one-off cost of the total maintenance backlog for 71,505 council-managed road bridges in Britain is £5.55bn.

Current practice of asset management and maintenance of bridges principally relies on information obtained through periodic visual inspections as a basis for establishing repair and maintenance programmes.  Visual inspections are qualitative, prone to human error and fail to estimate the true strength reserve of bridges.

The project

Researchers are collaborating with Network Rail to deliver the UK's first truly smart bridges. The structural configurations of the two monitored bridges are common types that exist on the UK transport network; one is a composite insitu concrete infill slab on precast prestressed concrete girders and the other is a composite bridge with steel girders and cast-in-situ reinforced concrete deck slab.

Data from the smart bridges will be collected using Microsoft Azure. Data scientists at The Alan Turing Institute will perform high level integrated data analytics on the continuously collected data, which will then be assessed and modelled by structural engineers at Cambridge. Combining structural and data-driven engineering brings new opportunities to smart infrastructure. Integrating these methods to take a data-centric approach delivers accurate and verified structural performance information to enable improved decision-making for asset managers.

Each bridge has the potential to generate up to 12GB data per day. Part of this project will be to identify which of the data collected are important to retain and which can be discarded, and developing automated techniques for culling unimportant data between train events and potentially from train events which provide no additional detail.

The combination of advanced BIM modelling, advanced finite element models and data analytics as part of this project brings opportunity for development of advanced digital twin bridge models.

Data collected from the monitoring systems will be interpreted using integrated algorithms to provide additional insights into the site-specific conditions. For example, by using sensing systems to and algorithms to create a Bridge Weigh-In-Motion system, researchers aim to predict the axle weights of passing trains using the deformation response of the bridge to a traversing train. Having traffic loading information will not only benefit operators for overload monitoring and control purposes but will also be useful to generate an accurate digital representation of the structure. The traffic loading information will be imposed back into the calibrated physics-based model, which will exist in the digital twin system, to predict the stresses and deformations throughout the structure, including un-instrumented locations. The system will not only be able to predict the load rating factor of the entire structure but identify the critical components for maintenance to secure better planning and efficiencies.Process innovation

Researchers plan to securely store information generated from the digital twin system on a BIM model in the Microsoft Azure cloud computing platform which will be processed using artificial intelligence algorithms to extract useful information collected from multiple parts of the structure and fed back into the system to be further refined. Implementation of machine learning will allow the continuous simulation of various damage ‘what-if’ scenarios and conditions and generate a deterioration model of the bridge. Such a model would be particularly useful for the bridge owners to proactively plan maintenance.

Challenges

Bridges are complex structures with hard-to-see details and are exposed to changing temperatures, moisture and dynamic loading making them difficult to inspect. Manual bridge inspection is a subjective, laborious, incomplete and costly procedure. In addition, inspections near busy roads or railways, or at height or over water bring additional risk and require protective equipment and equipment such as mobile elevating work platforms. Collecting and analysing data from a bridge monitoring system provides greater levels of detail and information without health and safety risks associated with on-site visual inspections. 

Timeline

Currently researchers are working with Network Rail to install the permanent power supply at the bridge sites to enable building continuous data acquisition systems. Data analysers will acquire data from the sensing system which will be transferred via 4G connection to the database in the Microsoft Azure cloud platform where data analytics will be implemented by the digital twin system. 

Collaborators

  • Network Rail
  • The Alan Turing Institute

"Cambridge University is developing a condition monitoring system for the UK's first truly intelligent bridges in collaboration with Network Rail, Microsoft and The Alan Turing Institute to showcase the practical application of real-time data analytics integrated with digital twins to benefit the bridge operators in maintaining their assets. The project is a key platform for enabling whole life cycle monitoring of Network Rail's transport infrastructure and has the potential of transforming asset management practices."

Nataliya Aleksieva, Senior Engineer, Network Rail

Industry Impact

Learnings from this project will be valuable to other bridge owners including Highways England, Transport for London and local authorities throughout the UK. Outcomes could also inform future monitoring schemes for upcoming rail and road infrastructure projects. In the longer term, this project could transform the way asset owners approach the inspection and monitoring of bridge stock. Information about the structures emerging from data analytics supports performance-based design leading to lower-carbon and lower-waste throughout the whole life of the assets.

Researchers will develop case studies for industry to present their findings and to share knowledge. A wrap-up workshop will invite all project stakeholders to discuss value and lessons learned which will also be shared though industry publications and talks to regional and national meetings of the Institution of Civil Engineers.

Wider benefits

The bridges have been constructed with integrated fibre optic sensor systems enabling a degree of monitoring not previously possible. The project provides an industry case study for: 

  • use of real time data analytics and integration with digital twins 
  • maximisation of the benefits of the prototype bridge monitoring systems 
  • demonstrating the capability of the monitoring system for remote, real- time condition assessment over the lifetime of a structure. 

The information delivered from the project will demonstrate the value of developing data-centric engineering solutions for smarter infrastructure. Organisations which own bridges as part of an asset portfolio will be able to refer to the outcomes from this project to better evaluate investment in monitoring and maintaining bridge stock to potentially increase safety and efficiency while reducing carbon and waste.

The whole life monitoring of the Staffordshire bridges will provide data that could inform future design, construction, operation and maintenance.  Looking to the future as part of the National Digital Twin programme [3], digital twins connected across the rail network will unlock value for all stakeholders and support better outcomes for services delivered by infrastructure for society[4].

Information from monitoring and data analytics will lead to efficiencies in the design, construction, operation and maintenance of the UK bridge stock, and beyond.

Meet the research team

Lead: Professor Campbell Middleton, Director, Laing O’Rourke Centre for Construction Engineering & Technology, University of Cambridge; Dr Jennifer Schooling OBE, Director, Centre for Smart Infrastructure and Construction, University of Cambridge

Team: Dr Farhad Huseynov, Senior Research Associate, Centre for Smart Infrastructure and Construction and Laing O’Rourke Centre for Construction Engineering & Technology; Paul Fidler, Computer Associate, Centre for Smart Infrastructure and Construction; Dr Didem Gürdür Broo and Dr Vladimir Vilde, Research Associates, Centre for Smart Infrastructure and Construction

 “The difference this research will make to industry is compelling. Digital twin technology will help the operators to better understand the structural behavior of bridges and reveal their remaining service life. Up until now, as an industry we haven't been able to do it. Now we have this opportunity to achieve something astounding.”

 

Dr Farhad Huseynov, Senior Research Associate, University of Cambridge

Engage

To find out more about this project contact Dr Farhad Huseynov at fh392@eng.cam.ac.uk

Visit the project page for this research here.

References:

[1] https://www-smartinfrastructure.eng.cam.ac.uk/news/staffordshire-allianc...

[2] https://www.racfoundation.org/media-centre/small-fall-in-number-of-subst...

[3] https://www.cdbb.cam.ac.uk/what-we-do/national-digital-twin-programme

[4] https://www.cdbb.cam.ac.uk/news/flourishing-systems

Need to Know

  • A survey of UK infrastructure revealed almost 20 per cent of bridge stock is reported as structurally deficient in some form based on visual inspections (Das P (1997) Safety of bridges. Telford, London)
  • The economic effect of traffic disruption due to bridge failure can be enormous. The cost of traffic disruption caused by closure of the Forth Road Bridge in 2016 was estimated at £1M/day for the duration of the closure
  • Digitising asset information of the UK’s rail network could save up to £770M over the next eight years and £8.9bn in direct benefits to the UK through public sector open data (National Infrastructure Commission ‘Data for the public good’ report)