- Start date: 11 February 2016
- End date: 10 August 2020
- Funder: EPSRC
- Value: £5,374,638
- Co-investigators: Professor Mark Birkin
National infrastructure provides essential services to a modern economy: energy, transport, digital communications, water supply, flood protection, and waste water / solid waste collection, treatment and disposal. The OECD estimates that globally US$53 trillion of infrastructure investment will be needed by 2030. The UK's National Infrastructure Plan set out over £460 billion of investment in the next decade, but is not yet known what effect that investment will have on the quality and reliability of national infrastructure services, the size of the economy, the resilience of society or its impacts upon the environment.
Such a gap in knowledge exists because of the sheer complexity of infrastructure networks and their interactions with people and the environment. That means that there is too much guesswork, and too many untested assumptions in the planning, appraisal and design of infrastructure, from European energy networks to local drainage systems.
Our vision is for infrastructure decisions to be guided by systems analysis. When this vision is realised, decision makers will have access to, and visualisation of, information that tells them how all infrastructure systems are performing. They will have models that help to pinpoint vulnerabilities and quantify the risks of failure. They will be able to perform 'what-if' analysis of proposed investments and explore the effects of future uncertainties, such as population growth, new technologies and climate change.
The UK Infrastructure Transitions Research Consortium (ITRC) is a consortium of seven UK universities, led by the University of Oxford, which has developed unique capability in infrastructure systems analysis, modelling and decision making. Thanks to an EPSRC Programme Grant (2011-2015) the ITRC has developed and demonstrated the world's first family of national infrastructure system models (NISMOD) for analysis and long-term planning of interdependent infrastructure systems. The research is already beng used by utility companies, engineering consultants, the Institution of Civil Engineers and many parts of the UK government, to analyse risks and inform billions of pounds worth of better infrastructure decisions.
Infrastructure UK is now using NISMOD to analyse the National Infrastructure Plan. The aim of MISTRAL is to develop and demonstrate a highly integrated analytics capability to inform strategic infrastructure decision making across scales, from local to global. MISTRAL will thereby radically extend infrastructure systems analysis capability: - Downscale: from ITRC's pioneering representation of national networks to the UK's 25.7 million households and 5.2 million businesses, representing the infrastructure services they demand and the multi-scale networks through which these services are delivered. - Upscale: from the national perspective to incorporate global interconnections via telecommunications, transport and energy networks. - Across-scale: to other national settings outside the UK, where infrastructure needs are greatest and where systems analysis represents a huge business opportunity for UK engineering firms.
These research challenges urgently need to be tackled because infrastructure systems are interconnected across scales and prolific technological innovation is now occurring that will exploit, or may threaten, that interconnectedness. MISTRAL will push the frontiers of system research in order to quantify these opportunities and risks, providing the evidence needed to plan, invest in and design modern, sustainable and resilient infrastructure services. Five years ago, proposing theory, methodology and network models that stretched from the household to the globe, and from the UK to different national contexts would not have been credible. Now the opportunity for multi-scale modelling is coming into sight, and ITRC, perhaps uniquely, has the capacity and ambition to take on that challenge in the MISTRAL programme.