This week’s emerging theme topic, digital twin, brings together most of the emerging themes (and other content) you have learned about over the last few weeks.

The basic idea behind a digital twin is to build a virtual version of a real world system by integrating a wide range of datasets and models. The twin allows you to examine the way the system works and to see the effects of potential changes. They may also incorporate machine learning are are able to learn and change over time as new information is added.

For example, a digital twin of an aircraft engine allows engineers to understand maintenance needs and performance issues under real world and modelled conditions. For example, Rolls-Royce feeds inflight sensor and instrument data via satellite link its digital twin.

Rolls-Royce UltraFan TurboFan - Source: Rolls-Royce

Read this short article on Rolls-Royce’s IntellgentEngine program: How Digital Twin technology can enhance Aviation

You may hear digital twin talked about in the context of the “multiverse”. This language is a bit trendy, but the basic point is that a digital twin provides a way of creating / testing out new ideas or looking at problems in different (endless??) ways. A basic example might be modelling the potential impact of different road intersection options on pedestrian safety. On a much broader scale, and in an emergency management context, a digital twin may be used to understand the cascading impacts of major flooding in an urban area. Impacts that may not be obvious using traditional GIS or statistical analysis.

 

What is a Digital Twin?

WATCH

TAKE A QUICK LOOK / KEEP FOR REFERENCE

Have a quick look at these two websites that provide some detailed information about Digital Twin from the point of view of two software developers in this space.  

• Digital Twins Explained: A Guide for the Built Environment from the New Zealand Company, 12d Synergy. This guide is also available for download. 
• Next look at ESRIs (more flashy!) Digital Twin website and go ahead and download a copy of their eBook for future reference. 

Take note of how familiar geospatial and data science methods and technologies are used in the context of a Digital Twin. 

 

Digital Twin – Examples

 

Now, look at this short video and have a play with the New South Wales Digital Twin

New South Wales Digital Twin

Source:  New South Wales Digital Twin

 

Now spend a few minutes exploring the data sets and tools on the New South Wales Digital Twin web portal 

End your exploration with this short article about how the NSW Digital Twin to inform emergency planning this bushfire season.  

 

Climate Resilience Demonstrator

The following video and interactive app were created as part of The Digital Twin (DT) Hub by the Centre for Digital Britain at the University of Cambridge. It will probably make you think about the scenario development group project you completed a couple of weeks ago. Start by watching the video and then move on to the interactive app.  

 

Now, work through the interactive app.

 

TAKE A QUICK LOOK / KEEP FOR REFERENCE

If you are interested in taking a deeper dive into the topic of Digital Twin, you may want to look at the follow recent journal papers. They provide nice reviews of the history of DT and their applications in disaster and emergency managment. No need to read these carefully - Just skim / have a look at tables and figures. Note PDF versions are on the following page in Canvas.  

 

Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management

 

Chao Fan, Cheng Zhang, Alex Yahja, Ali Mostafavi 2021. International Journal of Information Management, Volume 56
Abstract: This paper presents a vision for a Disaster City Digital Twin paradigm that can: (i) enable interdisciplinary convergence in the field of crisis informatics and information and communication technology (ICT) in disaster management; (ii) integrate artificial intelligence (AI) algorithms and approaches to improve situation assessment, decision making, and coordination among various stakeholders; and (iii) enable increased visibility into network dynamics of complex disaster management and humanitarian actions. The number of humanitarian relief actions is growing due to the increased frequency of natural and man-made crises. Various streams of research across different disciplines have focused on ICT and AI solutions for enhancing disaster management processes. However, most of the existing research is fragmented without a common vision towards a converging paradigm. Recognizing this, this paper presents the Disaster City Digital Twin as a unifying paradigm. The four main components of the proposed Digital Twin paradigm include: multi-data sensing for data collection, data integration and analytics, multi-actor game-theoretic decision making, and dynamic network analysis. For each component, the current state of the art related to AI methods and approaches are examined and gaps are identified.
Keywords: Digital twin; Machine learning; Information flow; Disaster management

 

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities 

Yu, D., He, Z. 2022. Nat Hazards 112, 1–36 (2022).

Natural hazards, which have the potential to cause catastrophic damage and loss to infrastructure, have increased significantly in recent decades. Thus, the construction demand for disaster prevention and mitigation for infrastructure (DPMI) systems is increasing. Many studies have applied intelligence technologies to solve key aspects of infrastructure, such as design, construction, disaster prevention and mitigation, and rescue and recovery; however, systematic construction is still lacking. Digital twin (DT) is one of the most promising technologies for multi-stage management which has great potential to solve the above challenges. This paper initially puts forward a scientific concept, in which DT drives the construction of intelligent disaster prevention and mitigation for infrastructure (IDPMI) systematically. To begin with, a scientific review of DT and IDPMI is performed, where the development of DT is summarized and a DT-based life cycle of infrastructures is defined. In addition, the intelligence technologies used in disaster management are key reviewed and their relative merits are illustrated. Furthermore, the development and technical feasibility of DT-driven IDPMI are illustrated by reviewing the relevant practice of DT in infrastructure. In conclusion, a scientific framework of DT-IDPMI is programmed, which not only provides some guidance for the deep integration between DT and IDPMI but also identifies the challenges that inspire the professional community to advance these techniques to address them in future research.

 

RESPOND

• What stands out to you about the Digital Twin approach?
• Do these examples meet the your expectations or definitions of DT from the previous readings?
• Do you think we can achieve spatial Digital Twins as robust as the Rolls-Royce IntellgentEngine? Does GeoAI help?
• What stages of EM can DT be used to help with? How do DT let you plan for the future? New normals?
• Can DT help us understand/model multi-hazard, compounding, cascading events?