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  1. Digital Twin Toolkit

    The purpose of this toolkit is to help you on your digital twin journey. It is intended to help you and your team think about why you need a digital twin and what it can be used for.
    DT Hub members asked for support in making the business case for digital twins so through the  Gemini programme, we put together a team of volunteers who are working in the area of digital twins and who offered their contribution on a pro bono basis to the development of a DT Toolkit.
    The result is a DT toolkit report which takes you through:
    ·        What is a digital twin?
    ·        What can digital twins be used for?
    ·        A case study register on the DT Hub
    ·        A business case template for a digital twin
    ·        A roadmap to implementing your digital twin
    This is the first version of the DT toolkit and we’re looking for your involvement in testing this toolkit and developing it further. Please comment here.
    The toolkit includes a business case template (available below or upon clicking "download this file"), which is intended to help you put together the business case for a digital twin.
    To watch the Sarah Hayes' presentation and the DT Toolkit Launch click here.
     
     
    DT toolkit business case template.docx

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  2. Pathway Toward an Information Management Framework: Summary of Consultation Responses

    In May 2020 the National Digital Twin programme (NDTp) published the Pathway to an Information Management Framework (IMF). The publication was accompanied by an open consultation to seek feedback on our proposed approach and to hear from across the community about how they thought the IMF should develop to support their use and adoption of it.  
    The consultation ran until the end of August, with ongoing engagement with the programme’s technical stakeholders, we received a great deal of valuable feedback. The full summary of the IMF Pathway Consultation Responses is published here today, written by Miranda Sharp, NDTp Commons Lead. 

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  3. Conceptually expanding and testing future expert elicitation methodologies and its outputs in CReDo

    CReDo is a climate change adaptation digital twin that brings together data from the energy (UK Power Networks), water (Anglian Water) and telecommunications (BT Group) sectors with weather event data with the aim to understand infrastructure interdependencies, asset and system climate derived failure impacts.
    In order to generate relevant insights to support our partner’s needs, CReDo needs to resolve what the impact will be on individual assets. That said, the highly interdependent nature of these infrastructure networks, such as key water assets relying on power supply assets to be operational, mean that to reliably model the impact of weather events, CReDo also needs to resolve how individual asset failures will cascade and impact other assets that are connected to them (both in their own and other infrastructure networks) This ability to obtain cross-sector insights is CReDo’s main pathway to creating value and impact in the UK infrastructure landscape.
    This report focuses on one component of the CReDo modelling and insight generation process, the individual asset modelling. The work done in phase 1 (please refer to the CReDo technical report 3: assessing asset failure available in the DT Hub for more detail) demonstrated how it is possible to elicit from asset owners the probabilities that each of their assets might fail in a particular future flood scenario. It also demonstrated how to use the outputs of an expert elicitation methodology to build Bayesian Network models that could potentially be implemented in CReDo. This report outlines the follow-up foundational work that was completed during the second phase of the project (in the 2022/23 financial year); this work should be taken as a further piece of development for the concepts introduced during the first phase. To fully understand this report and its implications, it is advised to read the phase 1 report that was introduced above.
    This document is only intended to provide a high-level view of the additional development work done during CReDo Phase 2. This has been focused on investigating how to overcome some of the drawbacks of the expert elicitation method that was tested in the first phase of the project. This document will:
     
    ·         provide a high-level description of the modified methodology used for expert elicitation sessions in phase 2.
    ·         list the lessons learnt obtained by running new expert elicitation sessions using the modified methodology.
    ·         outline the outputs and data obtained by using the modified methodology without providing any details that could be considered sensitive by asset owners.
    ·         provide a general view of the next steps needed to further develop this area of CReDo.

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  4. CReDo Phase 2: Developing decision-support use cases

    CReDo Phase 2 final report: Developing decision-support use cases
    The Phase 2 final report sets out the groundwork for CReDo to develop as a decision-support and cost-benefit analysis tool for the strategic resilience planning use case. In summary:
    CReDo can be a helpful decision-support tool for asset operators and regulators
    CReDo brings together data from different infrastructure asset operators to model the impact of extreme weather events, taking account of interdependencies within and across infrastructure boundaries. CReDo can be used by asset operators and regulators to make more informed decisions about where best to take action for the benefit of the infrastructure system as a whole (a so-called ‘connected approach’). In Phase 1, we designed an economic evaluation methodology to simulate the potential net benefits of CReDo’s strategic resilience planning use case. We found that CReDo, as a connected digital twin, had the potential to bring a range of benefits to asset operators, their customers and wider society by enabling asset operators to identify cross-network dependencies and pool their strategic investments. The current phase of CReDo (Phase 2) has contributed to the development of CReDo as a decision-support tool by identifying cross-network interdependencies and where coordinated investments across asset operators can achieve a given level of resilience at lower cost.
    In Phase 2, CReDo has developed to better reflect realities facing asset operators
    During Phase 2 of the project, we focused on developing CReDo to better reflect the realities facing asset operators. Real asset data from UK Power Networks, Anglian Water Group and BT Group was used to characterise the current resilience properties of their networks, including the costs of asset failures to their business and customers, and to reflect the incremental measures that they could undertake at the asset level to improve resilience. We then applied the economic evaluation methodology developed in Phase 1 to this data and compared the potential net benefits of different resilience strategies, from both an individual operator perspective and a system perspective. This economic evaluation is based on a set of cost models that quantify the benefits of avoiding flood-induced asset failures for infrastructure owners, customers and wider society.
    The outputs from Phase 2 could help overcome coordination challenges for resilience planning
    One of the key outputs from this phase of work is the CReDo measure of ‘asset criticality’. CReDo estimates the criticality of individual assets from a system perspective and an individual ‘siloed’ asset operator perspective by taking account of the total economic costs that are incurred if the asset fails as a result of direct flooding or cascading failures from other assets, whilst also accounting for existing levels of resilience in the system. This measure illustrates where and how a connected approach is likely to add value when making strategic investment decisions, compared to a world where asset operators make those decisions independently of one another. Other outputs from this phase include identifying the pathways of cascading asset outages and the budget impact of resilience investments.
    We simulated a case study flood scenario in East England as an illustration of the outputs that CReDo is able to produce
    To demonstrate the current decision-support functionality of CReDo, we simulated the impact of different investment decisions for a flood scenario in an area within the East of England. This case study showed that asset operators may prioritise interventions differently depending on their assessment of the criticality of their assets for their networks compared to the criticality of their assets for the system. In particular, we found that a connected approach to system planning can lead to better economic outcomes for a given level of resilience investment, as the system view can identify interventions with larger net benefits by prioritising assets with greater system criticality.
    This phase of work also identified further ways that CReDo can add value to decision makers
    This phase of work also identified further ways that CReDo can add value to decision-makers. For example, in the future, CReDo may be able to run numerous flood scenarios for a given intervention strategy and approximate the overall expected net benefits of that investment. Additionally, future phases may consider operational response measures, such as rediverting network flows or deploying mobile resources to affected areas, by incorporating inputs such as average response times, site access and other operational factors.
    Download this report file using the yellow button to read in full.
    Read more about CReDo
    The CReDo Phase 2 final report has been prepared for the project by Frontier Economics.

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  5. CReDo Phase 2: Strategic Outline Case

    This document forms a Strategic Outline Case for the CReDo project, structured per the Five Case model, a standard public sector approach to forming business cases. Typically, the business cases progress through three stages: first a Strategic Outline Case to assess the overall fit with policy and objectives, and comparing expected societal benefits; then an Outline Business Case adding deeper detail including on procurement options, market readiness and cost projections; and finally a Full Business Case at point of contract award with deeper detail including full costings and risks to be managed.
    The purpose is to cover the main aspects of assessing a proposed project’s viability and value for money whilst recognising that depth and details become available iteratively over time, as the project progresses.

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  6. CReDo Transport Use Case Dependencies

    Energy, water, and telecoms are only a subset of the infrastructure networks we need to consider in planning for climate resilience. To protect the whole infrastructure system, CReDo needs to expand into new sectors in a consistent, scalable way.   
    Building on previous phases of CReDo, we’ve developed a methodology for defining use cases for CReDo in new sectors. We used strategic planning for flooding on the road and rail networks as a case study to develop this methodology: flooding for ease of integration with the existing CReDo tool, the road network for its importance to current asset owners, and the rail network for its strategic importance to the UK economy. This infographic illustrates the outputs of our work.
    Our use case definition methodology is as follows:
    1.      Explore the decisions that CReDo might support within a sector, and what decisions would be enabled in CReDo’s core sectors by onboarding this sector.  
    Each infrastructure sector has a different array of options for climate adaptation and a different asset base at risk of climate events. As a tool for climate resilience planners, CReDo needs to take these particularities into account. Additionally, given the interdependencies between infrastructure networks, onboarding a new sector’s asset base into CReDo also has implications for CReDo’s existing models, which need to be mapped to realise the benefits of the tool.   
    Climate change adaptation reports from infrastructure operators are key to understanding which assets are at risk, how these are currently protected, and what high-level dependencies exist with other sectors. Conversations with stakeholders further illuminate the risks specific assets face. 
    2.      Create and refine a knowledge graph of assets within a sector.  
    CReDo represents assets and the connections between them in a knowledge graph, which is used to model the criticality and vulnerability of assets. A sector-specific knowledge graph is a key output of research into climate resilience decisions in a new sector. With a knowledge graph, we can identify how we might model the failures of different assets and their knock-on effects, and in turn, what asset datasets are required from an operator within the sector.  
    3.      Identify key stakeholders to engage throughout the onboarding process.  
    Every CReDo project requires engaging with the same stakeholder categories:  
    ·         Strategic climate resilience planners: These are ultimately the target users of CReDo within a given infrastructure operator. Continued engagement is necessary to ensure that CReDo develops in line with their decision-making needs. 
    ·         Asset data teams: We turn to asset data teams to understand whether the required data is available and can be shared. 
    ·         Asset engineers and managers: These engineers are integral to CReDo’s asset failure modelling approach. Where no in-house asset failure models or past data exist, we run elicitation sessions with asset strategy engineers to build our own models of asset failure. 
    Get involved  
    Download our infographic to see sample outputs of our use case definition methodology. 
    With a clear methodology for defining new use cases, we hope we can scale CReDo to more asset owners and other key users. Contact us via the Digital Twin Hub if you’re interested in a discovery phase. Alternatively, share your feedback on our use case development methodology. 

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  7. Developing CReDo from Demonstrator to a Market-Ready tool

    Authored by Tomas Marsh, Systems Engineer at Connected Places Catapult, this report discusses the future development requirements to ready CReDo for market from its current Phase 2 state (Sept 2023).
    CReDo (the Climate Resilience Demonstrator) is a connected digital twin seeking to improve system-wide resilience across infrastructure networks against the effects of climate change. CReDo has incredible potential to make real world impact and scale across multiple use cases including different climate scenarios, different asset types and sectors, and geographical expansion.
    In its current Phase 2 state, CReDo utilises integrated data across water networks, telecom networks, power networks, and healthcare networks. Using extreme weather event modelling, direct and cascading failures of the critical infrastructure assets are identified.
    This report focuses on the development gaps to be addressed to deliver CReDo from its current demonstrator state to a market-ready tool ready for commercialisation. The development requirements are considered through multiple lenses, including technology developments, potential funding avenues, and business process challenges.
    It is comprised of the following sections:
    Gap Analysis
    Through summarising the current state of CReDo and drawing a direct comparison to the desired future, market-ready state, the development gaps under five key categories have been discussed. This acts a useful tool to summarise the areas for development that will be addressed during upcoming milestones.
    Roadmap to Minimum Marketable Product
    The roadmap provides a high-level plan of the tasks to be undertaken to address the identified gaps. With each swim lane representing a gap category, the sequencing of tasks and interdependencies are clearly displayed with a summary of future state as the determined end point. A future potential vision for CReDo has not been neglected and is consolidated in the final column of the roadmap.
    Potential Funding Opportunities
    Whilst CReDo has received the majority of its historic funding through Connected Places Catapult milestones, its alignment with industry innovation objectives has allowed it to access multiple collaborative research and development funds. This section of the report explores existing funds which align with CReDo’s aims and could pose as future potential funding opportunities and enable industry to collaborate closely in the development of CReDo. 
    Cross-Sector Funding Mechanisms
    Readying CReDo for market is more than just a technology development challenge, a number of process challenges will need to be overcome to enable CReDo to efficiently embed into business-as-usual (BAU) for infrastructure operators. This section of the report considers one key hurdle from this category: mechanisms for funding cross-sector infrastructure resilience upgrades. Three potential concept mechanisms have been developed that could act as the basis for a future model for funding.
    This report is intended to be a high-level executive summary of the future of CReDo’s development. It provides a snapshot of the current thinking and alignment between project partners but is not intended to be a strict project plan for upcoming milestones. We welcome your feedback and comments for areas that may have been missed or that may not align with market requirements. Please download the report and provide us feedback in the forum on the CReDo network page.

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  8. Exploring net zero data strategies in the built environment

    This paper has been researched and produced by the Open Data Institute (ODI) in collaboration with its partner, Arup, and is published in June 2023.
    The paper explores an emerging area of thought in both data strategy and climate action. It is intended to spark discussion around the need for a carbon data strategy for a net zero economy, or a ‘net zero data strategy’, and how it might be put into practice, unearth examples of net zero data strategies – especially in the built environment – and encourage collaborative working to build momentum in the sector.
    The research undertaken included a review of the data landscape and identification of case studies, and informal discussions with partners in the space. It builds on our previous work on data strategy, the value of data sharing, data infrastructure for climate action, and data sharing in the built environment.

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  9. CReDo phase 2: Technical Report - Distributed Architecture

    Authored by CReDo’s technical partner CMCL, this report focuses on the implementation made to CReDo during Phase 2 of the project (April 2022-March 2023)
    CReDo (the Climate Resilience Demonstrator) is a connected digital twin seeking to improve system-wide resilience across infrastructure networks against the effects of climate change. CReDo has incredible potential to make real world impact and scale across multiple use cases including different climate scenarios, different asset types and sectors, and geographical expansion.
    At the end of Phase 2, CReDo has integrated data from 4 industry sectors: water networks (Anglian Water), telecoms networks (BT Group), power networks (UK Power Networks) and healthcare networks (NHS); and utilised flooding climate scenarios from Fathom (a water risk intelligence SME) data: coastal, fluvial and pluvial flood hazards geographically based in Norfolk.
    This report mainly explores why and how the team moved CReDo’s data architecture from the centralised data sharing architecture used in Phase 1 to a distributed data architecture by the end of Phase 2, and then goes on to explore considerations for future phases of the project.
    It documents:
    ·         Implementation of a distributed data architecture where asset owners can host their data assets in their own secure environments and CReDo can access the relevant data and return insights securely.
    ·         How the distributed architecture is deployed to enable extensible cross-sector sharing of data.
    ·         Generation of insights to facilitate decision support to improve climate resilience of the resulting combined infrastructure network.
    The report utilises narrative, diagrams, tables and screenshots to take the reader through:
    ·         CReDo’s architecture and data structure
    o   Hosting requirements and benefits of distributed over centralised data architecture
    o   Building CReDo’s knowledge graph, and how ontologies have been used generally and for each sector network
    o   Diagrammatic illustration of the overall knowledge graph
    o   Relation between scenarios and datasets, and how that is viewed in the user interface
    o   Locations of data components and processing functionalities in the distributed system
    o   Visualisation and the user interface
    ·         Future recommendations
    o   Summarising what has been achieved
    o   How CReDo can be extended in the future
    o   Considerations for what barriers may need to be overcome
    Additionally, within this report you can also find:
    ·         An explanation of what CReDo is and what it seeks to achieve (p.2,3)
    ·         Objectives of Phase 2 of the CReDo project (p.2,5)
    ·         Source code under permissive open-source licence (p.23)

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  11. The Wind of Change Is Blowing on Renewables, Making Them Cheaper and More Efficient

    Article by Bentley Systems

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  12. How the World’s First Digital Twin of a Nation Can Help Create Better Cities

    Article by Bentley Systems

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  16. The Nine Euro Ticket

    Article by Bentley Systems

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  17. Could a National Framework for Data Help Overcome the Shortcomings of the COVID-19 Census?

    Article by Bentley Systems

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  18. A Smarter Way to Future-proof Our Water Supply

    Article by Bentley Systems

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  19. Transforming and Decarbonising Infrastructure Delivery

    Article by Bentley Systems

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  21. Cyber-Physical Infrastructure Consultation

    From the Department for Science, Innovation and Technology
    Executive Summary
    From collaborative swarms of drones packing our food, to interactive virtual representations of operational hospitals, cyber-physical systems and their increasing interconnection are transforming our world at an increasing rate1.
    Our consultation last year explored the opportunities and challenges of a national Cyber-Physical Infrastructure, in which connected networks of such systems could provide a step change in the economic and social value of the individual applications.
    A significant number of written responses from industry (including both developers and users), academia, the wider public sector and wider society, supplemented by extensive online and in-person dialogue has informed this response.
    The strategic value and opportunities of Cyber-Physical Infrastructure were strongly endorsed by respondents, particularly highlighting: Innovation and productivity; Resilience; Climate change response; and Levelling up.
    Responses highlighted opportunities across a range of sectors, recognising the breadth and cross-sectoral potential of Cyber-Physical Infrastructure. However, the opportunities within the following sectors were identified most prominently: Energy Systems and utilities; Infrastructure and Built Environment; Manufacturing; Natural Environment; Transport and Supply Chains; and Wellbeing, Health and Social Care. Two cross cutting areas of Research, Development and Innovation, and Net Zero were also strongly identified (see Section 5 for more detail).
    There was also a strong call for government to help tackle a number of systemic challenges, through the supporting key enablers, namely: Security & resilience; Interoperability; Recognised value propositions; Frameworks, guidance and standardisation; and Skills (see Section 6 for more detail).
    This consultation response sets our vision to enable greater innovation in the UK through a Cyber Physical Infrastructure (see Section 4) and the key next steps that we and wider public sectors partners will continue to take in collaboration with industry, academia and wider society to realise this, including:
    • Launching a grant competition to fund one or more organisations working together to develop and host a Cyber-Physical Infrastructure ecosystem accelerating capability
    • Continued UKRI funding of a breadth of cyber-physical research, development and innovation including: o £3m to develop a multi-disciplinary UK digital twinning research community
    o Additional funding for digital twinning research to support and improve the operation and resilience of the UK energy grid
    o A suite of Catapult-led Cyber-Physical Infrastructure projects
    o Up to £20m for a research hub in digital twinning for decarbonisation and improved integration of the UK’s transport systemo £7.5 million in cyber security research with partners including the National Cyber Security Centre
    o A Turing Research and Innovation Cluster (TRIC) in Digital Twins
    • Department for Transport investing in digital twins for transport
    • Department for Business and Trade continuing to lead delivery of the National Digital Twin Programme
     
    1 https://www.gov.uk/government/publications/cyber-physical-infrastructure 

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  22. Public resources on DAFNI

    The Data and Analytics Facility for National Infrastructure (DAFNI) provided storage and computing capabilities via their public portal, and a secure private development environment to enable collaboration on sensitive data across the distributed team. The version of the CReDo model hosted on DAFNI is designed to be used with data stored on the platform and provides users the opportunity to run the visualisations with their own data, or to integrate with an alternative analysis pipeline.
    A document detailing public resources on DAFNI is available to download here

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  23. How to access CReDo on DAFNI platform

    The Data and Analytics Facility for National Infrastructure (DAFNI) provided storage and computing capabilities via their public portal, and a secure private development environment to enable collaboration on sensitive data across the distributed team. The version of the CReDo model hosted on DAFNI is designed to be used with data stored on the platform and provides users the opportunity to run the visualisations with their own data, or to integrate with an alternative analysis pipeline.
    For information about how to access and use the CReDo model on DAFNI, please download this document.

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  24. CReDo Technical Report 4: Modelling System Impact

    Summary
    Infrastructure assets in different sectors require connectivity in order to operate and provide essential services to end users. For example, water requires power and telecoms, power requires telecoms, among other connections. Understanding the connectivity of different infrastructure assets at the systems level, as well as which connections and assets are critical, is essential to ensure that investment planning is targeted at the most critical assets for normal and unusual operation. CReDo’s ambition is to address the connections between different infrastructure assets owned and operated by different asset owners in order to understand where the critical connections at the systems level may be, how critical assets may fail during extreme flooding scenarios and what is the level of vulnerability and impact on service provision.
    In this use case, asset data from three service providers delivering power, water and telecommunication services have been integrated and interrogated within a single platform to provide insights into sectoral interdependencies and better understand how the system responds to and is impacted by a range of potential future flood events, driven by a changing climate. This first phase of CReDo involves a modelling approach that assesses how the different assets are connected in the wider system and how cascading failures are propagating across the system following asset failures in parts of the network.
    The approach taken to analyse the flood impacts first used a flood-depth criteria for high-level identification of assets that would directly fail from high flood levels across the site. Building on this, the core of the work was to propagate failures from those directly impacted nodes. Failures are triggered from the direct connections between assets (a power asset providing external power supply to water assets) when a single asset fails as a result of flooding. The model developed is deterministic. The propagation of failure is implemented first within each sectoral network independently (water, power and telecommunication) before being extended across networks. This was achieved by integrating the data into component networks models and connecting these with an overarching coordinating algorithm.
    Building on the work undertaken in this first phase of CReDo, it is recommended that the next phases consider the following to address the current limitations. These include:
     
    Modelling of more complex interdependencies in the system (including for links and dependencies on the transport sector); Modelling of existing redundancy in individual assets, for example a flood defence wall or backup power supplies; Modelling of criticality of individual assets and how system vulnerability may be expressed; Running of a series of simulations at the system level to better understand how the system responds under a range of possible climate impact and individual asset failure scenarios; Development of dynamic models that simulate system impacts over time; recognising that the system is not static, and failures will unfold as the event progresses and recovery methods are put in place.  
    Testing the system under various scenarios with these criticalities identified would provide useful insights to asset owners/operators for making investment decisions that maximise system resilience in the face of extreme flood and other climate hazards driven by a changing climate.
    Finally, where the current work focussed on developing and testing failure and propagation models at a local scale, future work should allow for an implementation at a larger scale where the Information Management Framework associated tools can be scaled up and adapted to wider geographies and portfolio of assets.

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  25. CReDo Technical Report 3: Assessing Asset Failure

    Summary
    Planning for resilience against climate hazards in our economic infrastructure requires a systems approach. The development of connected digital twins that enable the sharing of information between infrastructure sectors and organisations will improve our understanding of how assets fail, where they fail and how any cascading failures can better inform resilience planning at the systems-level. To understand the connections between different assets in a system — and how a cascading failure could impact service provision — the vulnerability and probability of failure of individual assets and failure modes against different climatic hazards need to be understood.
    In this use case, the vulnerability of individual assets within an infrastructure system has been considered as the susceptibility of an asset to failure, its condition, capacity and ability to cope in the presence of the hazard, and it is expressed in probabilistic terms. Thus, asset failure following exposure to coastal flooding has been assessed using probabilistic modelling. This allows for the consistent interrogation of how individual assets in the system could fail under various incidents induced by coastal flooding. Singular characteristics of some assets and asset networks require distinct probability distributions that consider specific causal pathways that would lead the asset to fail. Bayesian Network Modelling uses probabilities to represent all uncertainties that are quantified for the modelling, and was adopted as the common language to model probability of failure and express vulnerability caused by coastal flooding at the asset-level.
    This report outlines the general approach undertaken in the connected digital twin to assess the probability of failure of assets within the network and how it has been applied specifically to investigate the probability of failure of an individual wastewater pumping station against coastal flooding.
    The model for the probability of failure requires input in the form of flood information (extent, depth), supported by expert judgement. This is to understand the causal pathways leading to the failure of an asset in the example of a wastewater pumping station. The development of a probabilistic model that is sufficiently realistic to deliver the intended goal is paramount for understanding vulnerability at the asset level and how this can affect the wider infrastructure system. This requires elicitation from experts in asset owner/manager organisations. Engaging with experts who operate individual assets not only provides a detailed understanding of the functioning /non-functioning of an asset, it also allows investigation of probabilities of failure for individual asset components under various operating conditions and past incidents in collaboration with asset owners. Through this engagement, a calibration of the probabilistic models can be undertaken for various sites and asset classes.
    The work conducted focusses on how Bayesian Network Modelling can be implemented across the asset network, by understanding and integrating a range of failure modes into a working interface. Key to the failure of an asset is the direct exposure of its main parts to coastal flooding. Elements such as the submergibility of parts, their location above the standard level of protection of the asset or how the asset operates during increasing flows in the wastewater network as a result of the coastal flooding conditions would have an impact on any direct failure assessment. Other indirect failure modes highlighted in the findings originate from externalities to individual assets, including its accessibility and thus its reliance on transport infrastructures to implement pre-incident and post-incident recovery efforts. Similarly, the dependence of an asset on external power supply and telecommunication provision for its functioning could lead to its failure if such external connections were cut prior or during a flood incident.
    The modelling work conducted in this phase of work considers maximum flooding conditions occurring at the same time across the entire network and leading to the worst disruption. It is recommended that future asset vulnerability modelling should focus on both the asset-specific and external factors that influence the probability of failure, allowing for the sequencing of failures as the flood event unfolds and alongside other associated climatic hazards. Investigations of how quickly an asset can recover after failing is another aspect that should be taken into account in the evaluation of its vulnerability. This would further support the system-wide vulnerability and cascading failure assessments.
    Overall, the deployment of Bayesian Network Modelling across the network demonstrates the benefits of having an overarching framework for assessing vulnerability of individual assets. Such assessments could be improved by investigating different combinations of failure modes across assets, and testing probability of failures across multiple climate scenarios, within the same interface. Whilst such an approach allows for a level of fine-tuning to calibrate site-specific probability distributions, the greatest benefits to resilience and investment planning decisions would be to implement the approach at the systems level. This would involve a multi-sectoral network of hundreds to thousands of assets.
     
     
     

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