- Opens: Monday 23 May 2022
- Number of places: 4
- Duration: 3.5 years
- Funding: Home fee, Equipment costs, Travel costs, Stipend
OverviewThe PhD student will work closely with our industrial partners to develop innovative digital tools and methodologies for improving the resilience and sustainability of critical infrastructure. These will include but not limited to the development of: mathematical models to address the performance of critical functions and critical hazards; new uncertainty methods with quantifiable confidence; fast simulation tools based on machine learning and engineering-physics based automatic learning.
- Masters level degree (MEng, MPhys, MSc) or equivalent (minimum 2:1)
- Knowledge of coding in any language.
- Desire to work collegiately, be involved in outreach
Digital twins are virtual life replica of components and systems and they are becoming increasingly popular tools to predict, monitor and control the behaviour of complex systems. Digital twins and machine learning are often used to reduce the necessary requirements and experimental analysis during the design phase, to test the behaviour of the system under critical situations, and to create scenarios that are difficult or impractical to recreate in practice. A realistic digital twin is constituted by dynamic models, constantly updated with different streams of data and information and able to predict (simulate) the performance of the system with the required level of confidence.
Digital twin needs data to be constructed. But data are usually not reliable because data can be imprecise, incomplete, truncated, missing, censored, corrupted, just to mention a few problems. It is therefore necessary to complement our digital model with physics-based rules and to explicitly account for the uncertainty. Models are only an approximation of reality and their accuracy need to be estimated and considered. Propagating the uncertainty through models is challenging for a non-expert in stochastic analysis probabilistic models. Handling large amounts of data is cumbersome, slow, and expensive.
Uncertainty analysis is too important to leave inexperienced people to do it themselves. Our calculation tools must do this automatically. Uncertainty analysis can be utilized to give the benefit of the doubt to people in uncertain cases where safety or fairness is a salient issue. Probabilistic methods are some of the most powerful methods available in computational science but are expensive and require certain expertise to perform correctly. Just as automatic differentiation has enabled machine learning, automatic uncertainty would enable cheap and speedy probabilistic calculations.
Aim & Objectives
Transport, communication, energy, and emergency services are critical infrastructures (CIs) to the function of society in any urban setting. Gaps in current scientific knowledge exist in our capacity to identify, model, and simulate critical interdependencies across heterogeneous systems under incomplete knowledge and uncertainty.
CIs are ageing and exposed to increased frequency and severity of natural hazards, and we have learnt from recent disaster-like events (COVID pandemic and the war in Ukraine) that unexpected events can quickly change the way infrastructures are used and designed for, and showing the complexity, interconnectivity, and dependencies between CIs. We cannot design or even maintain infrastructures that are able to sustain any possible threats. But we can add flexibility and adaptability to respond, adapt and mitigate the consequences of such events and recover quickly. A transformative shift in thinking is needed if we are to shift from maintaining and maximising individual functionalities of individual CI to understanding joint vulnerabilities and mutual interdependences and sharing the “excess of robustness and capacity” for the good of all. Resilience is the science of planning for the unknown. Unknowns are the domain of uncertainty management and quantification. Resilience thinking is required to address the modern unknown threats, interdependencies, and poor knowledge of systems.
Enablers for CI resilience and sustainability are:
- engineering modelling capability and mathematical approaches for assessing and understanding the impact of disruptions and cascading effects under deep uncertainty to facilitate the system of systems integration
- development of verified digital twins with an incredible level of details coupled with trustworthy AI solutions and simulations to make accountable and secure decisions
- data (with different level of confidence and precision) that might come from embedded distributed sensors or smart highly mobile devices and real-time data analytics able to provide vital information.
Those components will make CIs self-aware by providing damage assessment, self-repairing actions, and adaptation. Such highly intelligent and automatised CI also needs to interface with humans raising several criticalities including reliability, robustness and trust that need to be addressed.
This allows transforming current CIs into smart and intelligent infrastructure.
According to the student interest and background, the research will be aligned and supported by one of our industrial partners including:
- National Manufacturing Institute Scotland (Digital Factory): Our multidisciplinary team of experts work with companies to help them embrace the use of digital technologies. We offer solutions to help overcome roadblocks and inefficiencies, increase productivity, improve sustainability and push forward innovation within the manufacturing and engineering community.
Opening in 2022, our new state of the art Digital Factory will be a specialist technology centre built to enable a fully connected digital manufacturing environment.
- CRA Risk Analysis is one of UK’s largest integrated human factors, safety and risk consultancies in the UK, supporting major infrastructure projects across the world. For over 20 years, we have been supporting operational leaders and technical directors working in critical national infrastructure sectors.
- Galliford Try is one of the UK's leading construction groups, working to improve the UK’s built environment and delivering lasting change for the communities we work in. Galliford Try has a proactive approach towards digital driven processes and technology for an entirely digitised approach to project delivery, improving safety, quality and collaboration, and driving down carbon
Unique student experience
The students will be part of the Centre for Intelligent infrastructure (CII) and they will be part of a dynamic and multi-disciplinary team. Each student will receive multi-disciplinary supervision, with one supervisor from the CII and the other from Management School, or Security and Resilience Research Centre (SRRC) or mathematics and statistics (according to the student background and needs) plus one supervisor from the industry. The supervisory team will include at least one woman. Each student will spend between 3 to 6 months seconded to the industrial partner.
PhD students will receive subject-specific training via dedicated workshops, webinars and lectures provided by industrial and academic supervisors in:
- Uncertainty characterisation and quantification and Bayesian statistics
- Operational Research
- Artificial Intelligence and Machine Learning in Engineering
- Structural Health Monitoring
- Sustainable software and collaborative development
- Responsible for research and innovation course
An essential component of the training is the participation of Industrial case studies and hackathons designed to bring together academics and people from industry, business and NGOs to collaborate on real problems in workshops over one or a few days of intense interaction.
A training budget will also be provided to each individual PhD student.
(Research Excellence Award (REA) – EPSRC) - tuition fees (£16,733) and stipend for 3.5 years (£58,038).
Number of places: 4
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Start date: Oct 2021 - Sep 2022
Civil and Environmental Engineering
Programme: Civil and Environmental Engineering
Start date: Oct 2022 - Sep 2023
Civil and Environmental Engineering
Programme: Civil and Environmental Engineering