2018-2019, CDBB funded, with University of Sheffield and Newcastle University
Analysing Systems Interdependencies using a Digital Twin
Completed project (2018-2019), CDBB funded, with University of Sheffield and Newcastle University
Centre for Digital Built Britain (CDBB) General Research Project ‘Analysing Systems Interdependencies using a Digital Twin’
Analysing Systems Interdependencies using a Digital Twin - Long Chen
Prof. Jennifer Whyte, Imperial College (presented by Dr Long Chen) ‘Analysing Systems Interdependencies using a Digital Twin’ at the CDBB Summer Showcase on 12th September 2019.
Background: The need for a systems approach to infrastructure is emphasised in recent government policy, including Hackitt’s review and also the Lord’s inquiry into offsite manufacturing for construction. We know significant interdependencies arise between projects and existing infrastructure systems. However current methods for managing these interdependencies are inadequate for increasingly integrated and cyber-physical complex systems and do not translate well into a digitally enabled built environment. Better understanding of interdependencies becomes needed earlier in the process of delivery to attain the government strategy of “Transforming Construction” through an offsite approach to manufacturing for construction (as set out in the Industrial Strategy).
We anticipate developing new knowledge about how to configure digital twins for different use cases through our proposed research, which will abstact and use design-time models to facilitate analyses. Developing and using the rich data implied by the term ‘digital twin’ is relevant to projects to develop new infrastructure in the context of existing infrastructure systems.This work providesa first step toward next-generation systems engineering by demonstrating the feasibility of using a digital twin to generate new insight on systems relationships and interdependencies.
This step requires substantial interdisciplinary work and industry collaboration to examine the potential to combine a set of relevant analytic methods (network analyses, co-simulation, sensitivity analyses, visualisation). We have hence assembled an experienced team (Imperial College London, University of Sheffield, Newcastle University, with the Alan Turing Institute). The long-term ambition is to build the tools that decision-makers need to understand infrastructure system interdependencies within and across project boundaries.
Aim and objectives: The main aim is to articulate the extent to which a digital twin can be used to generate new insight on systems relationships and interdependencies. To do this, and develop well-targeted outputs, the associated objectives are to:
- Identify and rank the importance of critical interdependencies emerging in a project, both in the infrastructure system, and in the enabling production system;
- Develop new approaches to identifying critical interdependencies in time for decision-makers on the project to make decisions by linking digital data; and
- Articulate, across different scales, the utility of and practical barriers to the use of different analysis approaches (e.g. network analysis, co-modelling) in relation to practical problems and use cases faced in delivery.
Research programme: The team at Imperial College London has responsibility across the whole research programme, taking a lead on WP1 and WP2.
WP1 Quantifying interdependencies
WP2: Systems engineering and the digital twin
WP3: Network analyses and the digital twin
WP4: Co-modelling and the digital twin
WP5: Coordination and management
Across these work packages the teams in Imperial College London, University of Sheffield and Newcastle University will work closely together.
Outputs:
Whyte, J., Chen, L., Gamble, C., Genes, C., Pierce, K., Fitzgerald, J., Coca, D., Mayfield, M., Babovic, F., Pedro, A., Shah, N. (2019). Analysing Systems Interdependencies Using a Digital Twin, 2018/19 General Project funded by CDBB, Final Report.
Chen, L and Whyte, J. (2020) Analysing Interdependencies of Complex Engineering Systems using a Digital Twin-Driven Design Structure Matrix, 2020 ASCE Construction Research Congress (CRC 2020), Tempe, Arizona, USA.
Babovic, F., Chen, L., Gamble, C., Genes, C., Fitzgerald, J., Pierce, K., Punzo, G., and Whyte, J. (2019), A decision-tree for modelling approaches to use a digital twin in analysing systems interdependencies in infrastructure projects, Annual Systems Engineering Conference 2019, Leeds, UK, 19-20 September 2019.
Lu, Q., Chen, L., Li, S., and Pitt, M. (2020). Semi-automatic geometric digital twinning for existing buildings based on images and CAD drawings. Automation in Construction, 115, 103183. https://doi.org/10.1016/j.autcon.2020.103183
Chen, L., Lu, Q., and Zhao, X. (2020). Rethinking the Construction Schedule Risk of Infrastructure Projects Based on Dialectical Systems and Network Theory. Journal of Management in Engineering, 36(5), 04020066. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000829
Contribution and Impact:
Following the publication of the National Infrastructure Commission’s “Data for the Public Good” report, there are significant and growing research initiatives around developing digital twins, to which this research can contribute. This feasibility work delivers fundamental theoretical understanding that will support the use of the digital twin for systems analyses; and makes a practical contribution to the identification, prioritisation and management of interdependencies.
The outputs support the ambition to enhance the performance and resilience of built environments, to support the economic performance of UK construction and manufacturing firms and to positively impact the quality of life of citizens.
- The research community will benefit as this research will catalyse the development of next generation systems engineering, informing related research on the digital twin and providing a starting point for data scientists in both research and industry organizations. A measure of success is that it delivers publishable work that provides the basis for a major research bid.
- Commercial organizations involved in infrastructure and construction will benefit from the practical contributions to the understanding, prioritisation and management of interdependencies. We anticipate that there will be immediate impacts, in relation to better understanding of critical interdependencies, and impacts over a 5-10 year time-horizon as new research is developed and commercialised based on this work.
- Policy makers, in government and professional organizations, making decisions on the built environment will benefit from this research, as the theoretical understanding of use of the digital twin for systems analyses informs innovation and technology policies for the built environment. This is highly pertinent given the recent NIC and Hackitt reports, and ongoing ‘Transforming Construction’ programme.
Research Team
Imperial College London: Long Chen, Akeem Pedro, Nilay Shah , Jennifer Whyte
University of Sheffield: Dan Coca, Cristian Genes, Martin Mayfield
Newcastle University: Carl Gamble, John Fitzgerald, Ken Pierce