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Centre for Digital Built Britain completed its five-year mission and closed its doors at the end of September 2022

This website remains as a legacy of the achievements of our five-year foundational journey towards a digital built Britain
 

The Stafford Area Improvement Programme was a joint venture by Atkins, Laing O’Rourke, Volker Rail and Network Rail to remove a bottleneck in the West Coast Main Line that resulted in high-speed trains being impeded by slower local passenger services or by goods trains. To alleviate this bottleneck, a major upgrade of the line was undertaken, including the construction of 10 new bridges. The bridges built for the programme were designed by Atkins using Building Information Modelling (BIM) from the outset. The contractor, Laing O’Rourke, used off-site construction techniques wherever possible. 

The Centre for Smart Infrastructure and Construction (CSIC) and Laing O’Rourke Centre at Cambridge collaborated with Network Rail in the fibre-optic strain monitoring of two of the recently built railway bridges over the West Coast Mainline in Staffordshire. Implementing a real-time digital twin of one of the bridges, the research generated new information about load-bearing that is set to have a major impact on the costs of running the UK’s rail network safely, with benefits to Network Rail engineers and to all those seeking to travel or transport goods by train.

Process

Researchers began by working with Network Rail to install a permanent power supply at the bridge sites to enable continuous data acquisition systems, 24/7. The sensors are incorporated into the main girders, concrete deck, sleepers, web stiffeners and cross beams. The researchers also installed cameras to capture the corresponding visual footage of what is happening at each bridge.

With the power supply in place, data analysers acquired real-time data from the sensing system, transferred via 4G connection to the digital twin database. Developers built a cloud-compatible infrastructure to collect and process the data, pairing this with a web-based dashboard to enable asset managers (and before them, the researchers) to view and interpret real-time data and compare historic data. 

After gathering more than ten months’ worth of data, the researchers had evidence from 18,000 live trains. This unparalleled dataset continues to grow. Statistical analysis can now identify patterns and anomalies. One algorithm developed by the researchers assesses the weight of a passing train’s axles based on Bridge Weigh-In-Motion technology. The algorithm uses site monitoring data from the self-sensing bridge to predict the individual axle weights of passing trains on the fly while trains traverse the bridge site at high speeds. 

The final digital twin system implements data analytics in real-time, showcasing cloud-compatible data processing, with sophisticated algorithms handling real-time and historic data, visualised on a web-based dashboard that can be adapted for other digital twins.

Outputs

Publications and reports

  • Didem Gurdur-Broo, Miguel Bravo-Haro, Jennifer Schooling. Design and implementation of a smart infrastructure digital twin. Automation in Construction 136 (April 2022) 104171 https://doi.org/10.1016/j.autcon.2022.104171 (Summary: Digital twin implementations in smart infrastructure should include systems, information and organisational perspectives.)
  • Final Report for the CIH Information Management Integrated Project on the Development of UK’s first Real-time train load monitoring system on Staffordshire Bridges. 
  • Final Report for the CIH Information Management Integrated Project on the Staffordshire Bridges project.

Research papers

  • Paul Fidler, Farhad Huseynov, Miguel Bravo-Haro, Vladimir Vilde, Jennifer Schooling, Campbell Middleton. “Augmenting an existing railway bridge monitoring system with additional sensors to create a bridge weigh-in-motion system and digital twin”, 11th International Conference on Structural Health Monitoring of Intelligent Infrastructure. Montreal, Canada, August 8-12, 2022.
  • Farhad Huseynov, Paul Fidler, Miguel Bravo-Haro, Vladimir Vilde, Jennifer Schooling, Campbell Middleton. “Setting up a real-time train load monitoring system in the UK using Bridge Weigh-In Motion technology: a case study”, 11th International Conference on Structural Health Monitoring of Intelligent Infrastructure. Montreal, Canada, August 8-12, 2022.
  • Cong Ye, Farhad Huseynov, Liam Butler, Campbell Middleton, “Evaluating and visualising structural utilisation of an operational railway bridge using fibre optic sensing”. 11th International Conference on Structural Health Monitoring of Intelligent Infrastructure. Montreal, Canada, August 8-12, 2022.

So what?

Real-time information about loads and structural demands is a gamechanger. Researchers demonstrated that, even accounting for anomalies, the new bridges have more than four times the capacity needed for current train services. The biggest load passing over the bridges, a 750-metre freight train, demanded less than 15% of the structural capacity available. This knowledge gives asset owners options to improve efficiency, reduce costs, and make progress toward net zero. 

Future bridges could be built with one quarter of the structural support and still include a sufficient margin of error for safety. This would radically reduce the carbon footprint of bridges. Such findings are pertinent to current infrastructure projects including High Speed 2 and the enhancement of the trans-Pennine route in northern England.

Equally, as the real capacity of bridges is known, asset owners can plan to increase usage in line with that capacity. Loads can be safely increased, as can speed. This improves the carbon footprint for goods transported by rail, and means goods and people can transit from A to B more quickly. Meanwhile, the structural impact of such changes can be continuously assessed using the Staffordshire digital twins.

Every conceivable form of bridge construction is represented across Network Rail’s portfolio of 28,000, from simple stone slabs to large estuary crossings, such as the Forth Bridge. Managing a portfolio of this scale and diversity with frequent and extensive assessments is a considerable challenge. Until now, the primary means of evaluating these assets has been a combination of visual examination and Strength Capability Assessment. 

Information captured from the two instrumented bridges revolutionises what is and can be known about the real-time stresses on Network Rail infrastructure. Most immediately, the data from these bridges is relevant to every other bridge on the line. Processing that data lets asset managers learn about the infrastructural stresses of rail transport directly, informing strategic decisions about maintenance. As Chris Talbot, Principal Engineer at Network Rail, explains:

“With the instrumented bridges, we minimise the need for people to dangle off ropes. We get real-time data. We’re able to see how that trends. We can then start to predict where we need to intervene. We don’t incur costs for short-term planning, or short-term works. We can look at longer-term horizons. Data is a key asset for us going forward.”

The data has significant commercial potential. Understanding the detailed load demands of different train types enables infrastructure owners to vary the charges paid by train companies. This approach is well-suited to the UK railways, where railway infrastructure is managed by a national operator (Network Rail) while routes are franchised to different operators. (Compare how mileage assessments include a component of wear-and-tear, while road tax rates reflect the environmental impact of the given vehicle.)

There are opportunities for new industry actors too: The CSIC research team are already creating a digital twin for an existing bridge, applying experience from the Staffordshire work. As engineers learn to instrument existing infrastructure, they can make properly informed assessments and recommend the best options for maintenance, retrofit, and replacement. Where repair and retrofit proves possible, this saves carbon, money, and time, as well as reducing disruption to existing services. The assessment work is specialist and UK-based companies have an opportunity to be at the forefront of this new dimension of engineering.

The potential changes will make rail travel more efficient and cost-effective, with extra capacity released across UK railway infrastructure and a greatly reduced carbon footprint. The same should prove true for other bridge-dependent industries.

Industry impact

Bridge management is not the sole preserve of Network Rail. According to the latest analysis of the RAC Foundation, almost half of the nation’s 9000 bridges on motorways or A-roads show some signs of defects or damage that may significantly affect their capacity. Learnings from this project are valuable to bridge owners, including Highways England, Transport for London and local authorities throughout the UK. Outcomes should inform future monitoring schemes for upcoming rail and road infrastructure projects. In the longer term, this can transform how 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.

The researchers generated case studies to cover their findings, sharing knowledge with industry stakeholders. The values and lessons learned from the research were discussed with the stakeholders at numerous workshops, partner gathering events and face-to-face meetings. The results and the main findings from the project were presented to wider audiences at the Eleventh International Conference on Structural Health Monitoring of Intelligent Infrastructure (Montreal, Canada, August 2022) and demonstrated to key stakeholders in a by-invitation demonstration event on 6 September 2022. 
 

Wider benefits

The bridges were constructed with integrated fibre optic sensor systems enabling a degree of monitoring not previously possible. The project provided 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 serves to demonstrate the value of developing data-centric engineering solutions for smarter infrastructure. Organisations which own bridges as part of an asset portfolio can refer to the outcomes from this project to better evaluate investment in monitoring and maintaining bridge stock, and potentially increase safety and efficiency while reducing carbon and waste. 

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

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

This is not only good news for railway engineers and bridge owners. The findings should have positive impact on the lives of everyday citizens. Extending the life cycle of UK bridges also means reducing the quantity and frequency of engineering work that may disrupt those living or travelling in the locality. Lessening speed restrictions means getting from A to B a little faster, while relieving load restrictions will make it possible to transport more goods by rail more efficiently. 

Collaborators

  • Network Rail

"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