CM2 – Technical Challenges
Haritha Jayasinghe
EPSRC Centre for Doctoral Training in Future Infrastructure & Built
Environment: Resilience in a Changing World
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Name: Haritha Jayasinghe
College: Churchill College
Module Number: CM2
Module Title: Technical challenges
Coursework Number: 1
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CM2 – Technical Challenges
Haritha Jayasinghe
This essay covers applications of digitisation, interoperability and digital twins in
infrastructure and the built environment.
P art I: D igital Tw inning for the B uilt Environm ent
Based on lecture by. Prof. Ioannis Brilakis
Introduction
There are very few industries which have not been completely revolutionised by digitisation in
the past few decades. Yet most existing infrastructure systems are currently operated with
outdated technology and information. Of late, there has been renewed interest in digitisation
within the infrastructure domain, particularly catalysed by the covid pandemic. Fortunately,
infrastructure systems contain many opportunities for digitisation, ranging from virtual
simulations to pre-fabrication and collaboration. Most, if not all of these technologies are
brought together by the concept of digital twins.
A digital twin is a virtual instance of a physical system (twin) that is continually updated with
the latter’s performance, maintenance, and health status data throughout the physical system’s
life cycle.[1] There must be a bi-directional relationship between the digital and physical twins.
As the value of the digital twin is dependent upon the reliability of the data it contains, it is
crucial to ensure that a digital twin is kept up-to-date. The Singapore city digital twin serves
as an example of an outdated, hence unused digital twin. Furthermore, digital twins are used
throughout the lifecycle of the infrastructure system, specifically through design, construction,
operation and deconstruction.
Throughout these stages, digital twins offer a variety of use cases. During design, digital twins
are utilized to convey design intent and allow collaboration between stakeholders. In the
construction phase, they are useful for construction monitoring and efficiency enhancement,
pre-fabrication and construction automation, identification of faults etc.[2] As the
infrastructure moves into the operational phase, they will be utilized to facilitate inspections,
predictive maintenance and prevent unplanned shutdowns, handle emergency scenarios,
increase automation and monitor infrastructure and employee performance, speed up decision
making, improve interconnections between infrastructure systems and many other use cases.[3,
2, 4, 5, 6] Finally, once the system reaches end of life, digital twins can facilitate retrofitting,
more efficient deconstructions and provide material passports for re-use or recycling.[7]
C hallenges of technological uncertainty
Despite the variety of use cases offered by infrastructure digital twins, the concept is still in
its infancy. One of the key challenges to the adoption of digital twins is the lack of
standardisation across various digital twin platforms. The effects of this are twofold. Firstly,
the lack of standards leads to a variety of definitions on digital twins[2] with no clear criteria
as to what constitutes a digital twin. This in turn allows vendors to easily label their products
as digital twins, which causes infrastructure owners to lose confidence in digital twins, and
harm their understanding of the benefits of digital twins. This in turn hinders the adoption of
digital twins.
CM2 – Technical Challenges
Haritha Jayasinghe
Secondly, the lack of standards prevent the interoperability of digital twins.[8] One of the key
advantages of digital twins is the facilitation of exchange of information between infrastructure
systems. For instance, this could be between an offshore wind plant and a thermal power plant
communicating to balance the power grid output based on changing wind patterns, or a railway
and coach system communicating to handle excess passengers due to a breakdown in one of
the systems. The current lack of standardised formats leads to uncertainty and the existence
of multiple incompatible standards, preventing such interoperability between systems.
Another key challenge to the adoption of digital twins is the long life-span of infrastructure
systems, in contrast to the shorter life-span of various technologies. This inevitably leads to
technology built into infrastructure becoming outdated early in their life cycle. For instance,
many existing infrastructure facilities pre-date Computer Aided Design (CAD) and Building
Information Modelling (BIM) adoption, thus they lack the data to facilitate the creation of
digital twins. In fact, new infrastructure only accounts of around 0.5% of value added to
national infrastructure per year. The current process for creating digital twins for existing
infrastructure systems is extremely labour intensive and time-consuming, often negating the
perceived benefits of digital twins.[4]
Solutions
Tackling the above challenges requires the creation of industry wide standards for digital twins,
with clear definitions and criteria for interoperability, as well as the adoption of open source
formats.[8] Collaboration between various vendors who have traditionally utilized proprietary
formats is required. There is an emergence of Platform as a service (PaaS) vendors such as
Azure digital twins and Nvidia Omniverse which utilize open source formats, but further work
is needed to improve interoperability, and the models must be able to scale with their demands.
A key requirement in preventing the obsolescence of infrastructure facilities is designing flexible
infrastructure and developing methods of retrofitting existing infrastructure with newer
technologies. Recently, the advent of computer vision and deep learning has allowed the
automation of digital twin generation from point cloud scans of existing infrastructure. This
technology, while in its early stages, offers a cost-effective method for digital twinning of
existing facilities. The process consists of; capturing 3D scans, element identification, model
generation and enrichment.
In particular, element identification can be automated by using various 3D segmentation
methods to identify the various elements within the infrastructure, for instance the columns of
a bridge. Next, each element can be modelled, either by representing them with an idealized
model picked through a model repository, or by generating a custom mesh model to depict the
segmented element. The latter method can capture the nuances of the element at a high level
of detail. Finally, the model can be enriched by various additional information such as material
properties, defects such as cracks etc. The identification of such information can also be
automated through computer vision, for instance through automated crack detection using
images. Furthermore, a digital twin must be intrinsically linked to the physical twin, and
constantly updated. This can be achieved through integration with the various sensors within
the physical asset, as well as by periodic updates of data, for instance through regular scanning.
CM2 – Technical Challenges
Haritha Jayasinghe
Figure 1. Instance segmentation of bridge elements [13]
The above methodology has been utilized for the automated digital twinning of various types
of infrastructure ranging from bridges, roads, and railways to buildings and industrial facilities.
It is still in its early stages and only supports limited elements, and requires significant human
correction. Yet it provides a promising method to facilitate the digitisation of aging
infrastructure. In conclusion, standardisation and automation have become the key to ensuring
that infrastructure facilities can remain up-to-date and reap the benefits of digitisation in the
face of technological uncertainty.
P art II: Infrastructure, System of System s, B IM & D igital Tw ins
Based on lecture by Dr Jenifer Schooling
Introduction
Infrastructure plays a key role in the development of a society and the wellbeing of its people.
Many of the sustainable Development Goals such as Industry, Innovation and Infrastructure,
Clean Water and Sanitation, Affordable and Clean Energy, and Sustainable Cities and
Communities are intrinsically linked with infrastructure. However, infrastructure development
and operation have become increasingly challenging, due to the rapidly rising threat of global
warming, as well as economic constraints. As infrastructure faces increasing environmental,
economic, demographic and technological threats, better decision-making and efficient
operation have become crucial. Data is perhaps the most integral requirement to achieve this.
D ata for a w hole life perspective
As elaborated in part I, data is crucial in all stages of infrastructure from design to
deconstruction. Throughout the life cycle of an infrastructure project, various decisions must
be taken, which have both economic and environmental consequences. Data is vital to ensure
that informed decisions that minimizes the above concerns are taken. Such data may be
generated from a variety of sources ranging from embedded systems within infrastructure to
observed material flows and other process data, data captured through remote sensing and
other means or even social media. This creates a vast volume of data, which often overwhelms
facility operators, resulting in data being unnecessarily collected without deriving useful
inferences. Thus, proper curation of data, and designing pipelines that automate processes or
the generation of insights is crucial. Current advancements in machine learning are a key tool
for processing such large datasets.
CM2 – Technical Challenges
Haritha Jayasinghe
While such information has a vast variety of use cases, life-cycle analysis is particularly
noteworthy. Partially as a consequence of lack of data availability and the resultant
uncertainty, infrastructure operators often tend to ignore the entire life cycle of infrastructure
projects, and focus primarily on up front cost, both with regards to economic costs and carbon
costs, leading to sub-optimal performance during later years. This can also occur on a national
level, as a life cycle of an infrastructure project is far greater than that of a government term,
leading to optimisation of short-term goals. Now, with the availability of data, as well as
statistical and machine learning tools that can collate large amounts of data and draw
predictions, infrastructure owners can identify options that maximize utility over the entire
life cycle of a project, for instance sacrificing embedded carbon during construction for a more
energy efficient operation, through various design decisions. In addition, data can also be used
to predict impacts of one on various other interconnected infrastructure systems.
Interoperability and ‘System of System s’
The infrastructure system of a country is an amalgamation of traditional economic
infrastructure such as transport energy, etc., social infrastructure as well as the natural
environment. All of these are strongly linked with each other, forming a system of systems,
often with complicated links. For instance, whilst the relationship between a breakdown in a
section of railway overhead line equipment and a surge in vehicular traffic in the same area
may be somewhat obvious, the link between a power outage in an area, and increased traffic
near the local hospital due to electrically powered home medical equipment failure may be
harder to predict. Ideally, infrastructure control systems should be linked with automated
information flows, to ensure that the infrastructure can adapt rapidly to changing
requirements. An analogy can be drawn with a natural ecosystem, which returns to equilibrium
after an external threat through various complex material and energy flows.[9]
Unfortunately, most infrastructure systems currently operate within silos, with little data
sharing between infrastructure operators, which leads to inefficiencies in operations.[9] To
change this, a mindset shift is required, where operators proactively promote knowledge sharing
and interoperability between infrastructure systems. Furthermore, standardisation is required
to ensure that systems are compatible with each other. The Milton Keynes Smart Consortium
is an example of the creation of a data hub for sharing information from city wide infrastructure
systems.[9, 10] Ideally, all infrastructure systems within the nation should be coalesced into a
national digital twin, which includes both economic and social infrastructure systems, as well
as the natural environment.
In addition to the many use cases of individual digital twins presented in part I, such an
interconnected system of systems would be much more resilient to threats due to the ability
to better predict threats and identify courses of action using the available data. It could also
promote synergies between systems, both at an industry level (ex. load distribution between
transport systems) and across industries (ex. energy grid adapting to increased ‘work from
home’ due to a scheduled transportation system maintenance). While the concept of a ‘national
digital twin’ is far from reality, the Gemini Principles provide a strong foundation for its
creation.[11]
Specifically, the Gemini Principles emphasize the importance of the design of digital twins
being driven by clear purpose / use cases, the need to balance open data sharing with security
CM2 – Technical Challenges
Haritha Jayasinghe
and privacy of citizens, as well as the need to evolve, interoperate, and be governed with clear
standards. Ultimately, such digital twins must be created with the core focus of creating public
value. In addition to operational efficiencies, a national digital twin would facilitate various
simulations of impacts of decisions across the entire infrastructure system, resulting in more
informed long-term policy decisions. A vision of such a system was presented by the Climate
Resilience Demonstrator.[12]
Figure 2. Gemini principles by the Centre for Digital Built Britain [11]
In conclusion, data plays a key role in supporting a nation’s infrastructure, to the point where
data infrastructure has become a part of the national infrastructure system. As infrastructure
faces increasing challenges, data has become an essential tool to maintain infrastructure
resilience. Yet, adoption of a data-centric view to infrastructure management is severely lacking
across the UK. To achieve resilience, and maximize value to the public, infrastructure decisions
must be driven by a data backed life cycle analysis within individual systems, while
interoperability must be achieved among all systems within the nation.
R eferences
[1] A. Madni, C. Madni, S. Lucero Leveraging digital twin technology in model-based
systems engineering Systems, 7 (2019), p. 7, 10.3390/systems7010007
[2]
E. Agapaki, G. Miatt, and I. Brilakis, ‘Prioritizing object types for modelling existing
industrial facilities’, Automation in Construction, vol. 96. Elsevier BV, Dec. 2018. doi:
10.1016/j.autcon.2018.09.011.
[3]
V. Dilda, L. Mori, O. Noterdaeme, and J. V. Niel. Using advanced analytics to boost
productivity and
profitability in
chemical
manufacturing,
2018.
CM2 – Technical Challenges
Haritha Jayasinghe
Available: https://www.mckinsey.com/industries/chemicals/our-insights/usingadvanced-analytics-to-boost- productivity-and-profitability-in-chemical-manufacturing
[4]
E. Agapaki, ‘Automated Object Segmentation in Existing Industrial Facilities’, Apol–
o - University of Cambridge Repository, May 2020, doi: 10.17863/CAM.52102.
[5] Arayici Y. Towards building information modelling for existing structures. Struct surv
[Internet]. 2008;26(3):210–22. Doi: 10.1108/02630800810887108
[6] Cho YK, Alaskar S, Bode TA. BIM-integrated sustainable material and renewable
energy simulation. In: Construction Research Congress 2010. Reston, VA: American Society
of Civil Engineers; 2010.
[7] Volk R, Stengel J, Schultmann F. Building Information Modeling (BIM) for existing
buildings — Literature review and future needs. Autom Constr [Internet]. 2014;38:109–27.
doi: /10.1016/j.autcon.2013.10.023
[8] V. Piroumian, "Digital Twins: Universal Interoperability for the Digital Age," in
Computer, vol. 54, no. 1, pp. 61-69, Jan. 2021, doi: 10.1109/MC.2020.3032148.
[9] Grafius DR, Varga L, Jude S. Infrastructure Interdependencies: Opportunities from
Complexity [Internet]. Vol. 26, Journal of Infrastructure Systems. American Society of Civil
Engineers (ASCE); 2020. p. 04020036. Available from:
http://dx.doi.org/10.1061/(asce)is.1943-555x.0000575
[10] MK:Smart Consortium, 2017. Available: https://www.mksmart.org/about/
[11] The Gemini Principles, 2018. Center for Digitally Built Britain, available:
https://www.cdbb.cam.ac.uk/system/files/documents/TheGeminiPrinciples.pdf
[12] The National Digital Twin programme produces a film and interactive app for COP26
showcasing the role of connected digital twins in tackling the climate emergency, 2021.
Center for Digitally Built Britain, available: https://www.cdbb.cam.ac.uk/news/nationaldigital-twin-programme-produces-film-and-interactive-app-cop26-showcasing-role
[13] Riveiro B, DeJong MJ, Conde B. Automated processing of large point clouds for
structural health monitoring of masonry arch bridges [Internet]. Vol. 72, Automation in
Construction. Elsevier BV; 2016. p. 258–68. Available from:
http://dx.doi.org/10.1016/j.autcon.2016.02.009