Network Rail’s aspirations are to digitalise the condition monitoring of their assets, enabling non-subjective and data led decision making. Working with key stakeholders, Waldeck have developed and actively deployed techniques which offer improvements over the current means of asset condition monitoring and assessments.
The project has focused on digital means of data collection and the application of this data. The data is leveraged to support machine learning and artificial intelligence for condition assessments, automation of BIM models, and their aggregation into Network Rail’s ‘Digital Railway’ and asset management solution.
The solution has been developed to support a digital, data driven approach to the asset management of their 29,000+ bridge portfolio, providing holistic and lifecycle benefits to Network Rail. The solution sees digital data supporting informed decision making for project engineers and asset care teams over the entirety of each bridge’s lifecycle, as well as supporting Network Rail’s ambition to create a ‘Digital Railway’.
Following initial proof-of-concept trials, Waldeck and Nottingham Trent University are now on their 3rd project with Network Rail.
The beta development of the solution remains the focus of our latest project, which is also targeting the scalable deployment of the approach overall.
Focusing on employing a ‘golden thread’ approach to the data which is collected, the team worked collaboratively with Network Rail to understand the many limitations of the current methods, and how through a data-focussed approach this could be both improved and leveraged to provide wider reaching asset care benefits for Network Rail, as well as very much supporting their ‘Digital Railway’ aspirations.
Undertaking trials over a cross section of masonry bridge and viaduct assets, the team focussed on real-world application of digital data collection and its use within an engineering environment.
The initial trials worked to understand how engineering decisions could be better informed through the use and analysis of digital data. The beta development of the solution remains the focus of the latest project, which is also targeting the scalable deployment of the approach overall.
The adoption of leading digital surveying technologies offered many benefits to the approach overall. Previously, individual photographs would be taken of defects which were they appended to reports with engineer’s annotation. Whilst these do provide individual instance snap shots of the defect, they fall short in offering and holistic view of the asset to better understand their location and frequency.
Capturing geolocated 360-degree data sets of the structure and its immediate surrounding area has enabled the viewing of high-definition imagery, which via point cloud technology has allowed measurable and comparable assessments to be made of each assets condition. These added benefits support progressive assessments, allowing for accurate understanding of defect progression between surveys.
A key benefit of the approach considerably reduces the need for boots on ballast, which offers huge improvements from a health and safety perspective, enabling engineers to review and analyse the data remotely.
Working with these data sets has enabled the team to research and develop how machine learning techniques for object and defect recognition can also be integrated to support engineering assessments and the categorisation of the defects. The same approach has also enabled the team to explore visual programming techniques which automate the production of BIM Models from the survey data sets.
The project and technology utilised has led to wider conversations within Network Rail, seeing the team develop a bespoke asset viewing solution to maximise the engineering potential of this new approach.
Mark Greatrix, Director of Digital & Technologies at Waldeck shared:
“Being able to support Network Rail with their future aspirations and to take their visions into demonstratable working solutions over the past two years has been a prestigious project for Waldeck. The project has been founded on a strong and collaborative working relationship, which has certainly enabled the teams to deliver the best results.
“As we continue to work through the scalability of the approach and solution overall, we are working with Network Rail to release all of the ‘value adds’ which the works offer for the wider periphery of Network Rail stakeholders.”
Nataliya Aleksieva, Senior Engineer at Network Rail shared:
“The trials undertaken by Waldeck and their university partner on 50 masonry bridges in 2020 were not only to demonstrate the capability of the technologies but also to enable Network Rail engineers to holistically evaluate the condition of the structures off-site in their real environment. The project team were able to combine point cloud surveys undertaken by drones and terrestrial laser scanning with sufficient accuracy which provided a complete survey for the structures.
“The team has developed algorithms to create BIM models directly from the surveys to support the automation of the existing processes for determining the condition marking index for the structures and the development of the digital railway twin.
“This development is expected to bring significant benefits to Network Rail by minimising the traffic disruption, reducing boots on ballast, and obtaining richer data on our assets which will enable NR engineers to evaluate their condition more accurately.”