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Digital Twins

Short overview​

General Description​

“A digital twin is a virtual representation of an object or system designed to reflect a physical object accurately. It spans the object's lifecycle, is updated from real-time data and uses simulation, machine learning and reasoning to help make decisions.” (IBM 2024). They are used in several industries, such as in the health sector or aeronautics. In the construction industry for example, building information modelling (BIM) is used as another way to reflect physical assets (Feng et al. 2021). Digital twins are one of the most advance technologies to manage complex environments by facilitating connectivity through self-operative functionalities (Ariyachandra and Wedawatta 2023). “Smart City Digital Twins (SCDTs) are an emerging approach to understanding and addressing urban challenges” (Pan et al. 2024). They are a digital model of the city that functions on continuously collected data from infrastructure and organisational systems with the objective of understanding its functioning by simulating the real conditions. SCDTs have a multitude of applications, including, but not limited to, energy management, environmental monitoring, disaster response and traffic assessment in urban areas (Pan et al. 2024).

Building Information Modeling (BIM) The National BIM Standard-United States (NBIMS-USTM) defines BIM as “a digital representation of physical and functional characteristics of a facility” (Purnama et al. 2022). BIM is the process of capturing building-related information, and managing all gathered data, encompassing the entire lifecycle of a building (Wahba et al. 2024). The advancement of Building Information Modelling has provided increasingly comprehensive and accurate information about the built environment. Furthermore, the combination of BIM with big data from IoT sensors has enabled the creation of digital twin smart cities, which are accurate 3D models of cities (White et al. 2021).

Digital Twins and BIM are both used to enhance efficiency, though for different purposes. While digital twins require real-time data, BIM does not necessarily do so. They also differ in main user groups. BIM users are usually architects, engineers or facility managers (Feng et al. 2021), the digital twin user groups are broader and depend on the physical object to be reflected.

Potential for Climate Change Adaptation​

Digital twins can support climate change adaptation in urban areas in a number of ways. Digital twins of cities can help identify potential vulnerabilities such as extreme heat by detecting heat islands. They can also address the impacts of extreme weather events, such as the management of storm water drainage during storm events, based on simulations of the urban water drainage system, river and groundwater flow.

Digital twins can be used to explore and test climate change adaptation strategies in urban areas. They can analyse the effectiveness of different climate change adaptation strategies in real-world conditions by modelling future environmental conditions using AI and ML to explore how urban systems will cope under different local climate change scenarios (Henriksen et al. 2022).

Digital twins could be an important tool for effective climate change adaptation in cities by supporting effective adaptation strategies in an iterative process, covering all phases from assessment to implementation and monitoring (Ariyachandra and Wedawatta 2023).

Potential for Disaster Risk Management​

Advances in digital twin technology provide an opportunity to leverage digitalisation for better disaster risk management (Ariyachandra and Wedawatta 2023). Digital twin technology can be implemented and contribute to all aspects of the disaster risk management cycle.

Digital twins can be used in the planning phase of urban infrastructure and systems, enabling the design and implementation of resilient systems that prevent and mitigate the disaster risks in urban areas. The data analysis algorithms used by digital twins are key to building intelligent disaster prevention and mitigation systems (Yu and He 2022). The integration of IoT sensors and big data analytics into digital twins enables real-time monitoring of the critical infrastructure and environment conditions, this allows for the detection of any anomalies before disasters unfold. This allows for early-warning to emergency response units and communities at risk, via social media, in a timely and effective way (Ariyachandra and Wedawatta 2023).

Digital twins can support emergency response planning, by optimising resource allocation and the testing of response scenarios, considering the interconnectedness systems and complex human behaviour. During an emergency digital twins real-time monitoring capability helps to deploy the available emergency response units in the most sufficient way (Lagap and Ghaffarian 2024).

The use of digital twins has “substantial potential to revolutionize post-disaster risk management efforts and achieve resilient communities against the adverse effects of disasters” (Lagap and Ghaffarian 2024). The technology can support damage assessment by comparing the post-disaster state of urban infrastructure with pre-disaster models. Digital twins can also be used to simulate and evaluate different reconstruction scenarios. Reconstruction plans can be tested within the digital twin before implementation.

Application in different Climate Hazards​

Flooding​

Accurate flood modelling requires the use of a comprehensive three-dimensional topographic model, accompanied by the incorporation of the building information inherent to a digital twin. The integration of rainfall data and river levels into the digital twin enables the generation of a timeline of potential flooding events. This information obtained from the digital twin can then be fed back to the smart city and used to alert citizens to the timeline of a potential flood (White et al. 2021). Flood simulations can assist local authorities with flood planning by indicating which areas of a city will be most severely affected by a future flood event, thereby identifying areas that will require special attention in flood management and flood evacuation planning. The incorporation of historical flood data into the digital twin can inform the development of long-term flood prevention strategies as well (White et al. 2021).

Sea Level Rise​

Denmark’s DK-model hydrological information and prediction (HIP) system is considered as a digital twin that models the real time hydrological conditions in the country. The DK-model HIP includes a detailed digital twin of the urban catchment covering the greater Copenhagen area and Frederiksberg. The model provides hydrological information and boundary conditions for simulation of sea level rise issues, especially focussing on groundwater flooding, drainage of rainwater through sewage systems, saltwater intrusion, and water security in the greater Copenhagen area (Henriksen et al. 2022).

Landslide​

Digital Twin-empowered Landslide Emergency Risk Management: Hong Kong serves as the test site for a new city-scale slope digital twin, designed to detect, simulate and visualise landslides, thereby facilitating more effective landslide risk management. As the first model of its kind, the operating platform incorporates an augmented reality environment, enabling interactive decision-making to support emergency management and public education (HKUST 2023). The project's objective is to enhance the disaster resilience of existing slope safety systems in Hong Kong. This will be achieved by incorporating a combination of remote and ground-based monitoring techniques of the surface conditions into the digital twin, thereby allowing for the rapid detection of landslides and assisting with the emergency response, for example through the provision of evacuation routes (HKUST 2023).

Water Scarcity / Drought​

Digital twins can be supported by Earth observations tools, openly available ground data from online sensor networks, groundwater levels measurements, soil moisture, streamflow to create real-time groundwater and surface water real-time models. These models make predictions of future development and provide 5-10 days forecast of water condition. Thus, in conjunction with Digital twins, models predict water scarcity and droughts giving urban authorities more time to prepare and address the issue to increase water security of vulnerable people and infrastructure (Henriksen et al. 2022).

Strong Winds / Storms​

Digital twins, like the digital twin drainage masterplans implemented for the city of Lisbon, can address the effects of climate change, particularly the increase in storms and stormwater. The digital twin developed by the city allows for the simulation of different storm scenarios, helping the design of mitigation strategies for different return periods of storm events and to manage the storm water of major storm events. It thus supports DRM, as these events overwhelm the city’s current infrastructure, by providing efficient drainage alternatives (Riaz et al. 2023).

Forest / Bush Fires​

Data collected by fire sensors can be linked to digital twins to detect and inform authorities of fire events in real time. In the event of a fire the digital twin can guide residents and people in the area to evacuation routes and provide dispatched fire engines with the fastest route through traffic, making firefighting operations more efficient (Hyun et al. 2024).

Extreme Temperatures​

Smart City Digital Twins (SCDTs) can be used to monitor and predict collective urban heat exposure. Meteorological sensors can be utilised as data collection method to feed a model that assesses and forecasts extreme heat. When integrated with data pertaining to community activity, obtained from street camera systems, digital twins can be employed to develop strategies to mitigate community exposure to extreme heat (Pan et al. 2024).

Saltwater Penetration​

Denmark’s DK-model hydrological information and prediction (HIP) system is considered as a digital twin that is modelling the real time hydrological conditions in the country, this includes monitoring of saltwater intrusion in the greater Copenhagen area (Henriksen et al. 2022).

Application in DRM / CCA Measures​

Nature-based Solutions​

Nature-based solutions (NbS) are a sustainable approach to address urban challenges, providing ecosystem-based systems to increase urban climate resilience. Digital twin technology can reduce the complexity of monitoring different NbS, such as ecosystem-based approaches to urban drainage, by simulating and managing multiple forms of NbS. Digital twins can monitor environmental conditions such as water quality to assess the impact of NbS by integrating quantified improvements in water quality due to ecosystem based processes (Brasil et al. 2022).

Digital twins specialised on running simulations on NbS and digital twins focused on NbS design can be linked to evaluate the efficiency and their effectiveness (Brasil et al. 2022).

Integrated Coastal Zone Protection​

Coastal Zone Information Model (CZIM) is a digital twin of the coastal zone (Yu et al. 2024). The model represents a comprehensive approach that overtakes the coastal zone management. It does so from the underlying data collection and management to the integration of the data for feedback between the real coast and the virtual coast model. This approach requires a comprehensive framework for simulating, interpreting and optimizing of the coastal zone systems inside the digital twin. CZIM is designed to mitigate domain barriers in coastal information applications, share information with efficient standards, and apply information with intelligent and realistic interaction (Yu et al. 2024).

Stormwater Management​

Pipedream is an end-to-end simulation engine that enables the real-time modelling and state estimation of urban drainage networks (Bartos and Kerkez 2021). The software constructs a digital twin model of real-world stormwater networks, which utilises embedded sensors and online models to monitor drainage system dynamics in real-time. The pipedream software presents a new tool for flash flood nowcasting in urban environments. This addresses the limitations of gauge networks, which are utilised by many cities to detect flooding. These networks are typically deployed at larger streams, resulting in the existence of 'blind spots' within the drainage network. The software allows to more accurate estimation of localised flooding at ungauged locations through the interpolation of hydraulic states derived from locations where sensor data is available. Digital twin of stormwater networks can overcome the limitations of traditional management approaches that lack real-time data and have a limited understanding of the systems dynamics. Both limitations are solved by digital twins (Bartos and Kerkez 2021).

Waste Management​

A digital twin of a waste management system has the potential to serve as a decision-support system, thereby enhancing the operational efficiency of waste management in urban areas. The digital twin is provided with data from smart bins, which are equipped with fill-level sensor modules that supply the model with real-time information about the fill-level of each public waste bin (Barth et al. 2023). The digital twin determines the optimal time and location for waste collection operations. In addition to data regarding the status of waste bins, it is also using real-time data from collection vehicles, as well as of waste recyclers or incineration plants. The combination of these datasets enables it to provide the waste management systems’ real-time status along with optimal routes for collection vehicles. Thus, this results in increased efficiency (Barth et al. 2023).

Relevance within the Project Cycle​

BIM and digital twins can be particularly useful during project implementation and progress phases. Later, related data can inform review and evaluation phases.

Project Preparation:

If already available, digital twins can inform the design of projects, for example a digital twin used to model or forecast hazard events.

Project Implementation:

Digital twins can serve as decision-support systems throughout the implementation phase. They can help to adjust implementation action and enhance efficiency of projects.

Verification and Project Progress:

BIM and digital twins updated with real-time data can be a valuable source of information, particularly for technically complex projects.

Data from BIM and digital twins can later be used to compare project outcomes and impacts, thereby supporting progress tracking and evaluation.

Technology Requirements​

The implementation of a city’s digital twin requires different technical systems being in place as well as dedicated software, human resources and technical expertise to operate it.

Digital twins require a data collection layer, a network of IoT sensors to collect real-time information about urban systems and environmental conditions.

Real-time processing (data processing layer) is required to interpret the large amounts of data flowing into the system to update the digital twin. This can be supported by cloud computing.

Modelling and simulation software for predictive analysis by leveraging AI and ML software.

Communication networks are required to link all the IoT sensors with the central data processing. High-speed internet networks enable real-time data transmission and remote operation of systems and are thus required.

A user interface is required to visualise the city’s digital model.

Integration and interoperability of different systems and data standards are required to communicate and exchange data between different systems.

The use of Digital Twins or BIM must be reflected in the project’s construction and engineering contracts. The respective responsibilities of the parties must be carefully defined. With respect to BIM, contracts should define the role of a BIM manager in bigger projects. Further, since all engineers use BIM, effective intellectual property rights management is necessary (see RMMV Guidebook Section 2.3.4).

Both, Digital Twins and BIM tool must have adequate security to protect the collected data and to ensure that it is kept confidential. Data leaks and security breaches threaten the viability of using BIM software. All participating entities are required to ensure the ongoing confidentiality, integrity, availability, and resilience of processing systems and services. They need to ensure that their individual and collective IT security measures are adequate to withstand cyber-crime attacks. (see RMMV Guidebook Section 2.3.2).

If KfW (or persons acting on behalf of it) are (also) processing personal data, the privacy check in RMMV Guidebook Section 2.3.1 must be followed.

Summary Assessment​

Overall Effectiveness​

Digital twins offer a virtual representation of city systems that can simulate, predict, and respond to various urban challenges. Their ability to process real-time data from IoT sensors and other sources enables precise monitoring and management of urban infrastructure, making them highly effective for disaster risk management. By integrating historical data, they also support long-term planning for disaster prevention and climate adaptation. They are robust systems that can offer valuable insights but should be considered against contextualized information. Digital twins can become close to representations of real-world challenges but cannot be equalled to those. A critical perspective is required in decision-making to avoid imprecise or biased information generated by digital twins. Digital twins also allow for the analysis and testing of strategies during implementation. The iterative nature of digital twins supports continuous improvement, as they can integrate feedback to refine future actions.

Overall Efficiency​

“Digital twins optimize resource utilization and operational efficiencies within smart city governance through predictive analytics and optimization algorithms. By identifying inefficiencies and optimizing resource allocation, digital twins help cities overcome resource constraints and maximize the impact of limited financial, technical, and operational resources” (El-Agamy et al. 2024).

By leveraging advanced technologies like AI and machine learning, digital twins can automate certain urban processes, further enhancing efficiency. However, achieving this efficiency requires overcoming challenges such as high energy consumption and data processing demands. The integration of renewable energy sources and energy-efficient algorithms is essential to ensure that digital twins contribute to resilience without exacerbating environmental issues. Additionally, the interoperability among different systems can reduce redundancies and maximize the efficiency of digital twins.

Key Challenges and Limitations​

Hardware-related challenges correspond to the complexity of sensor integration into the digital twins and issues with hardware reliability, as well as the time costly updates sensors require. IT infrastructure limitations represent further challenges, as existing infrastructure often falls short of the requirements needed for the building of a smart city digital twins (El-Agamy et al. 2024; Wang et al. 2023; Lei et al. 2023).

Data-related challenges are mainly represented by data connectivity, with the main issue being data integration and data interoperability. Data specific limitations concern data quality, data accuracy, data availability and data standards. This can generate data bottlenecks in digital twins (El-Agamy et al. 2024; Wang et al. 2023; Lei et al. 2023).

Software-related challenges correspond to security vulnerabilities and the algorithmic complexity regarding software components. Further challenges come up with software combability and accessibility, especially licenses that limit the implementation of digital twins (El-Agamy et al. 2024; Lei et al. 2023).

Non-technical challenges: These include sensitivity regarding data security and regulations, as well as ownership and responsibility over the digital twin. Other non-technical challenges include the financing of digital twins, as the required systems are expensive to implement and the trustworthiness of the digital twin, especially when public participation of citizens is not considered in the implementation process (Lei et al. 2023). For more comprehensive information on human rights-related challenges and limitations, see the Principles for Digital Development, the Global Digital Compact, as well as Mejias and Couldry (2024).

Recommendations to optimize the Use of the Digital Tool​

Adopting standardized sensor interfaces to ensure system interoperability. Implementing predictive maintenance strategies to address reliability issues of the hardware required for digital twins. Adopting universal data standards and developing scalable data management systems to enhance the data interoperability in digital twins.

Utilising advanced analytical software tools and robust cybersecurity measures to increase the performance and security of digital twins (El-Agamy et al. 2024).

Taking ownership of the digital twin by using open data and software and implementing a data sharing network and promoting collaboration between organisations (Lei et al. 2023).

In order to identify and mitigate technology-related human rights risks within KfW-financed projects, we recommend to use the KfW Human Rights Check for Financial Development Cooperation during project preparation and implementation.

Project Examples / Use Cases​

  • The “Rotterdam 3D” project is a three-dimensional digital model of the city of Rotterdam published in 2022. The digital twin is a detailed copy of the real city, with most buildings, infrastructure and other objects, such as trees and lamp posts, displaying in 3D. The model is based on two-dimensional maps, height measurements, aerial photographs and administrative data of objects on the street. The model provides information about every building in the city, such as its address and year of construction. Possible applications of the digital twin of Rotterdam include running simulations to improve water safety and support municipal asset management, as well as city marketing applications. Other applications could include public space monitoring and environmental analysis. “Rotterdam 3D” is openly accessible via the internet: https://www.3drotterdam.nl/#/ (Ricciardi and Callegari 2023).
  • Urban areas around the globe are particularly vulnerable to the effects of climate change. Digital twins allow for urban issues, such as the impact of climate change on urban areas, to be simulated in digital space. Considering predicted strong increase in the number of hot days in the future for the city of Zurich, Switzerland, the issue of urban climate adaptation represents a significant challenge for urban planning. It is therefore essential that adaptation measures are implemented to counteract local overheating. To address this challenge, the city of Zurich has implemented a “Sectoral Planning Heat Reduction” strategy, which includes an analysis of the impact of planned buildings on the city’s cold air flows. The analysis is performed on a GIS-based 3D model that incorporates a range of digital twin data, including terrain models, planned and existing building structures, and tree locations. Meso- and micro-scale climate models are run on the digital twin to determine the obstruction effect of planned buildings. The digital twin-based analysis can measure the impact of the spatial characteristics of planned buildings on climate factors, including temperature and cold air flow, can be measured (Schrotter and Hürzeler 2020).

Linkages to other Tool Types​

  • Artificial Intelligence (AI): AI enables to perform simulations on the digital twin allowing it to make predictions of future states and process optimisations to be applied in the physical systems. The decision making-capability of AI can be leveraged to automate urban systems.
  • Data Sources: Big data technology harmonizes multiple data sources, handling great volumes of diverse formats. Digital twins can use Big Data as input for more comprehensive and accurate representations of physical systems. Given the high velocity of Big Data updates, they enable actualized simulations and monitoring within the digital twin. Big data, collected from IoT sensors but also social media postings can function as sources for the digital twin, making it more accurate.
  • Communication and Collaboration Tools: Digital twins and communication tools enable collaborative decision-making. Scenarios simulated can be shared easily and be discussed to inform decision-making. In the context of e-learning, digital twins can support scenario-based learning. Stakeholders can take advantage of these simulated scenarios and train for specific events. See also Collaboration and E-learning tools
  • Earth Observation/Geospatial Tools: BIM or Digital Twins, and GIS or Remote Sensing can be merged to visually map the physical infrastructure and the integration of geospatial data, such as land use and topography. See also Geospatial tools and GIS
  • Mobile tools: Mobile devices can interact with digital twins to provide data or to generate visualizations of specific data coverage patterns. See also Crowdsourcing Tools
  • (Remote) Management Information Systems (R/MIS): MIS can help in designing and managing infrastructure or urban layouts that are more resilient to climate and disaster risk impacts. See also Management Maintenance Systems (MMS) and (Remote) Management Information Systems
  • Internet of Things (IoT): IoT sensors (e.g. RFID and laser scanners) collect real-time data of the physical environment. They provide the digital twins with a constant flow of information needed to accurately reflect the physical urban system. See also Sensors / SmartMeters (Internet of Things)

Licence​

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

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