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  1. When we travel by train, we expect that we will arrive at our destination safely and on time. Safety and performance of their service network is therefore a key priority for Network Rail. Our latest video in the Digital Twin Journeys series tells the story of how researchers have inherited two intensively instrumented bridges and are transforming that high volume and velocity of data into a digital twin showing the wear and pressures on the bridges, as well as other information that can help the asset owners predict when maintenance will be required and meet their key priorities. Remote monitoring has several benefits over using human inspectors alone. Sensors reduce the subjectivity of monitoring. Factors such as light levels, weather and variations in alertness can change the subjective assessments made by human inspectors. They may also be able to identify issues arising before visual inspection can detect them by monitoring the stresses on the bridge. A human inspector will still be sent to site to follow up on what the remote sensing has indicated, and engineers will of course still need to perform maintenance. However, remote monitoring allows the asset owners to be smarter about how these human resources are deployed. One important insight for Network Rail is based on more accurate data about the loads the bridges are experiencing, and the research team have developed a combination of sensors to make a Bridge Weigh-In-Motion (B-WIM) Technology. As shown in the video, a combination of tilt sensors, bridge deformation and axle location sensors to calculate the weight of passing trains. As the accuracy of weight prediction data is impacted by changes to ambient humidity and temperature, sensors were added that detect these factors as well. Accelerometers were added to calculate rotational restraints at the boundary conditions to improve the accuracy of weight predictions and cameras were installed so that passing trains can be categorised by analysing the video footage.   The digital twin of the Staffordshire Bridges centres on a physics-based model for conducting structural analysis and load-carrying capacity assessments. The site-specific information, such as realistic loading conditions obtained by the sensors, will be fed into the physics-based model to simulate the real structure and provide the outputs of interest. A digital twin replica of the structure will be able to provide bridge engineers with any parameter of interest anywhere on the structure, including in non-instrumented locations. All of the sensors on these bridges produce a high volume of data at a high velocity. Without data curation, we could easily be overwhelmed by the volume of data they produce, but the research team is learning to narrow down to managing the right data in ways that provide the right insights at the right time. Working with Network Rail, this project 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 proactive maintenance programmes and optimise future designs, increasing safety and reliability across their whole portfolio of assets. You can read more from the Staffordshire Bridges project by visiting their research profile. This research forms part of the Centre for Digital Built Britain’s (CDBB) work at the University of Cambridge. It was enabled by the Construction Innovation Hub, of which CDBB is a core partner, and funded by UK Research and Innovation (UKRI) through the Industrial Strategy Challenge Fund (ISCF). To see more from the Digital Twin Journeys series, see the homepage on the CDBB website.
  2. On the Gemini Call on 16th November we had Sheikh Fakhar Khalid - @Khalid - Chief Scientist at Sensat, present a Feature Focus on 'Automation Enabled Digital Twins', which created a lot of discussion and questions! Khalid has kindly agreed to carry on the discussion of the key topics that came up. If you'd first like to watch his presentation, you can view it here. Here are the key questions that were asked, so that you can add your own thoughts and Khalid will be checking in to respond! (Note that we had a few questions around dimensions, so we have grouped them for you) What standard is applied to the data to achieve semantic interoperability? Or is this all just siloed tech? Is the 'digital thread' a thread through time or a thread through process? Or both? Is a digital twin 3D or 4D? Why does a digital twin need to be three dimensional? Surely the definition doesn't require that? On '3D', shouldn't we make a distinction between the digital twin and the visualisation of the digital twin? Is there a danger of using "levels". It makes "lower level" twins look less mature, even though that might be all that is needed for that use case. We saw that with BIM - where some "chased" the levels What do you think? Please share below... (And don't forget to register for next week's Gemini call here)
  3. Digital twins enable asset owners to use better information at the right time to make better decisions. Exploring the early stages of a digital twin journey – understanding the information need – are Staffordshire Bridges researcher Dr Farhad Huseynov and Head of Information Management Henry Fenby-Taylor. Network Rail manages over 28,000 bridges, with many being more than 150 years old. The primary means of evaluating the condition of the bridges is through two assessment programmes; visual examination and Strength Capability Assessment. Every conceivable form of bridge construction is represented across Network Rail’s portfolio of assets, from simple stone slabs to large estuary crossings, such as the Forth Bridge. Managing a portfolio of this diversity with frequent and extensive assessments is a considerable challenge. Condition monitoring The current process for condition monitoring involves visual examination by engineers and takes place every year, along with a more detailed examination every six years. The visual inspection provides a qualitative outcome and does not directly predict the bridge strength; it is conducted to keep a detailed record of visible changes that may indicate deterioration. The load-carrying capacity of bridges is evaluated every five years through a Strength Capability Assessment, conducted in three levels of detail: Level 1 is the simplest, using safety assumptions known to be conservatively over-cautious (i.e. 1-dimensional structural idealisation). Level 2 involves refined analysis and better structural idealisation (i.e. grillage model). This level may also include the use of data on material strength based on recent material tests, etc. Level 3 is the most sophisticated level of assessment, requiring bridge-specific traffic loading information based on a statistical model of the known traffic. Understanding the information and insights that asset owners require helps shape what data is needed and how frequently it should be collected – two essential factors in creating infrastructure that is genuinely smart. During the discussions with Network Rail, the research team found that Level 3 assessment is only used in exceptional circumstances. This is because there is no active live train load monitoring system on the network; hence there is no site-specific traffic loading information available for the majority of bridges. Instead, bridges failing Level 2 assessment are typically put under weight and/or speed restrictions, reducing their ability to contribute to the network. This means that there is potentially huge value in providing Level 3 assessment at key sites with greater frequency. Digital twins for condition assessment The Stafford Area Improvement Programme was setup to remove a bottleneck in the West Coast Main Line that resulted in high-speed trains being impeded by slower local passenger and goods trains. To increase network capacity and efficiency, a major upgrade of the line was undertaken, including the construction of 10 new bridges. Working with Atkins, Laing O’Rourke, Volker Rail and Network Rail, a research team including the Centre for Smart Infrastructure and Construction (CSIC), the Centre for Digital Built Britain (CDBB) and the Laing O’Rourke (LOR) Centre for Construction Engineering and Technology at the University of Cambridge is collaborating with Network Rail to find a digital twin solution for effective condition monitoring. Two bridges in the scheme were built with a variety of different sensors to create a prototype that would enable the team to understand their condition, performance and utilisation. Both bridges were densely instrumented with fibre optic sensors during construction, enabling the creation of a digital twin of the bridges in use. The digital twin’s objective is to provide an effective condition monitoring tool for asset and route managers, using the sensor array to generate data and derive insights. Identifying challenges and solutions Meetings were held with key stakeholders including route managers and infrastructure engineers at Network Rail to learn the main challenges they face in maintaining their bridge stock, and to discover what information they would ideally like to obtain from an effective condition monitoring tool. The team liaised closely with the key stakeholders throughout to make sure that they were developing valuable insights. Through discussions with Network Rail about the team’s work on the two instrumented bridges in the Staffordshire Bridges project the following fundamental issues and expected outcomes were identified: A better understanding of asset risks: How can these be predicted? What precursors can be measured and detected? A better understanding of individual asset behaviour Development of sensor technology with a lifespan and maintenance requirement congruent with the assets that they are monitoring How structural capability be calculated instantly on the receipt of new data from the field Development of a holistic system for the overall health monitoring and prognosis of structures assets Realistic traffic population data in the UK railway network. (Can this be predicted with sufficient accuracy for freight control and monitoring purposes?) To address these issues, the team instrumented one of the bridges with the following additional sensors, which, combined, produce a rich dataset: Rangefinder sensors to obtain the axle locations. A humidity and temperature sensor to improve the accuracy of weight predictions against variations in ambient temperature. Accelerometers to calculate rotational restraints at the boundary conditions and therefore improve the accuracy of weight predictions. Cameras to categorise passing trains. Data from these sensors feeds into a finite element model structural analysis digital twin that interprets the data and provides a range of insights about the performance of the bridge and the actual strain it has been put under. Applying insights to other bridges Significantly, information from the instrumented bridge sites is relevant to adjacent bridges on the same line. Having one bridge instrumented on a specific route would enable Level 3 assessment for other structures in their portfolio and those of other asset owners, including retaining walls, culverts, and other associated structures. Just as the new bridges relieved a service bottleneck, digital twins can resolve procedural and resource bottlenecks by enabling insights to be drawn about the condition of other assets that weren’t instrumented. This is a valuable insight for those developing their own digital twins, because given that one bridge is instrumented it follows that where trains cannot have diverted course, then any other bridges along that same stretch of track will be undergoing the same strain from the same trains. This insight will enable teams implementing sensors to be able to efficiently implement a sensor network across their own assets. One of the outcomes of the Staffordshire Bridges project is development towards a holistic approach for the overall health monitoring and prognosis of bridge stocks. Such changes improve workforce safety by reducing the requirement for costly site visits while maintaining a healthy bridge network. You can read more from the Staffordshire Bridges project by visiting their research profile. This research forms part of the Centre for Digital Built Britain’s (CDBB) work at the University of Cambridge. It was enabled by the Construction Innovation Hub, of which CDBB is a core partner, and funded by UK Research and Innovation (UKRI) through the Industrial Strategy Challenge Fund (ISCF). To keep up with the Digital Twin Journeys project, check out the Digital Twin Journeys home page.
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    The Climate Resilience Demonstrator (CReDo) project from the National Digital Twin programme is holding a webinar to launch the project to a global audience in conjunction with the COP26 climate conference on 2nd November at 10:30-12. This webinar replaces the weekly Gemini Call, and the DT Hub community are encouraged to sign up, as well as inviting their wider networks to attend. The climate emergency is here now, and connected digital twins are an important part of achieving net zero and climate resilience. The CReDo team will present how the project meets this urgent need, and will premiere two exciting outputs – a short film and an interactive visualisation of how connected data across three infrastructure networks can provide better insights and lead to better resilience of the system-of-systems overall. Only if we come together to securely share data across sectors can we plan a smarter, greener, more resilient built environment. Book your spot today! Keep an eye on the DT Hub website for updates about the CReDo programme.
  5. 35 downloads

    Over the past decade, we have witnessed an unprecedented transformation in the Architecture, Engineering, Construction and Operation (AECO) industry in the UK but also abroad. From the early use of collaborative 3D technologies mandated as part of the UK Government Construction Strategy in 2011 (put into practice in 2016) which certainly accelerated the adoption of Building Information Modelling (BIM); to the introduction and eventual fall from grace of Virtual Reality (VR), the buzzwords in this industry change as frequently as the trends at London Fashion Week. The nirvana of BIM supposedly promised the now infamous 20% construction cost savings that were nowhere to be seen. Therefore, there is no surprise the level of scepticism that any such a new concept receives. Thus, to avoid similar misconceptions from the past, we have contributed to the development of the Digital Twin Toolkit in order to first define what we mean by a digital twin and second to clarify the business case as well as the benefits this newly rediscovered concept brings. This whitepaper therefore expands on the toolkit by providing advice and suggestions from our own experience and the journey of the past ten years so as to avoid the same pitfalls that BIM has led to. A client claiming they have “BIM getting delivered next Thursday” only for the team to discover it is just a computer with a pre-installed Revit is one such example.
  6. (8) Data wrangling - importing 300+ datasets a quarter - YouTube Is this making the case for bread and butter digital transformation?
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    Join us on Friday at 3.30pm to hear on the power of visual data, third-party APIs and IoT integration, and how a system can provide complete situational awareness and project visibility to all key stakeholders in major capital projects. Hear how project teams can visualize the entire life of their CapEx project, and even beyond as a live asset. Enabling cross-organisational and cross-vendor workflows, planning of future worksite activities and tracking project milestones allows for big data to be managed in a slick, intuitive and digestible way. A system that allows users to visualize both in 2D and 3D their site/facility and combine IoT and GIS functionalities together to become the "single source of truth". https://attendee.gotowebinar.com/register/4510351039689315851
  8. One might argue that the foundation for any Digital twin is understanding what information is required for the business to exist and deliver on its strategy and client needs. Without this, how do we know what information to include in our Digital Twin and how our assets are performing in carrying out this objective? I'm delivering a 3 hours free webinar on the 12th August to show a simple method for extracting OIRs from an executive document and specifying what is critical to understanding the business benefits to owning a Digital Twin. be great if you can join me!https://lnkd.in/dxF6BEN
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    Premieres: August 3rd, 9am-10am PT with live Q&A and on-demand following the event. [Click Here for iCal Invite] Join us live to learn and ask questions! Deep Dives are interactive technical live and on-demand events for developers, architects, or anyone building IoT solutions. Learn how to get started with iModel.js, an open-source programming library from Bentley Systems to create living digital twins: https://www.imodeljs.org/ Microsoft engineers and guest speakers do technical deep dives about a new feature or scenario. List of all upcoming Microsoft IoT Deep Dives: https://aka.ms/iotshow/deepdive Learn more about Integrating 3D Models and IoT data In this session we will demonstrate an application that combines 3D models, 2D maps, and reality mesh into a single environment for visualization. Within that environment we will demonstrate a live, real time, seamless visualization of IoT data streams. Next we will walk through the architecture that enables the application. We will show how we have mapped the IoT data to the assets within the digital twin. And we will show how to keep the digital twin in step with engineering changes. This is done by automating the generation of the Digital Twin Description Language (DTDL) .JSON. Bentley's iTwin platform and iModel.js open-source programming library provide powerful capabilities for aggregating 3D, 2D, reality data and other sources to link with IoT data for a "single pane of glass" visualization, analytics, and simulation so users can make more effective decisions in a timely manner. By integrating the iTwin platform with Azure Digital Twins and other Azure IoT services, Bentley and Microsoft are making it easier for developers, integrators, and customers to build digital twins of their infrastructure assets. Guest Speakers: Josh Schifter – Sr. Software Development Manager, Bentley Systems Roop Saini – Software Developer, Bentley Systems Deep Dive Host: Pamela Cortez - Azure IoT Senior PM Resources: Get started with iModel.js: https://www.imodeljs.org/ Learn more about iTwin: https://www.bentley.com/en/products/product-line/digital-twins/itwin Details on how to ask questions during the live event Add the Deep Dive invite to your calendar or come back to this page when the premiere airs and go to https://aka.ms/deepdive/digital-twins-live (link will be redirected to YouTube before the event) for the streaming event to view the video and chat box Make sure to sign in to YouTube to be able to ask questions to our engineers and talk to others on the chat! Have an idea for an IoT Deep Dive? Tweet #IoTDeepDive @AltaOhms with your request!
  10. When asked by a relatively senior member of staff here what the Digital Twin is all about, and why they should care, I pulled together some SmartArt (pictured) to try to explain the component parts of an infrastructure organisation's twin. Keen to get the wider community's thoughts on this approach. Digital Twins are having a bit of moment here at Highways England, to the extent that our principle risk is not a lack of twins, but a surfeit of incompatible twins. I'm beginning to think that the ‘Digital Twin’ of a complex organisation such as HE will actually need to function as a hierarchical system of systems. We need to understand how our organisation functions and what it looks like from a conceptual data perspective (the Schema), we then need a single source of truth, preferably one structured as a graph to reflect the Ontology (the Data), and finally there will be the specific manifestations of the above for different parts of the business (e.g. BIM, digital product catalogues, design, porfolio management etc. etc.) which should be united by the common schema and data above.
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