2018-2019, CDBB funded, with University of Sheffield and Newcastle University


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.

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 will contribute. This feasibility work will deliver fundamental theoretical understanding that will support the use of the digital twin for systems analyses; and make a practical contribution to the identification, prioritisation and management of interdependencies.

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:

  1. Identify and rank the importance of critical interdependencies emerging in a project, both in the infrastructure system, and in the enabling production system;
  2. Develop new approaches to identifying critical interdependencies in time for decision-makers on the project to make decisions by linking digital data; and
  3. 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.

Expected outputs:
1)    theoretical understanding of use of the digital twin for systems analyses; and
2)    practical contributions to the understanding, prioritisation and management of interdependencies.

Key dates:
January 2019  Interim Report
April 2019       Interim Report
July 2019        Final Report

Research Team
Imperial College London: Chen Long,  Nilay Shah , Jennifer Whyte
University of Sheffield: Dan Coca, Martin Mayfield
Newcastle University: John Fitzgerald, Ken Pierce