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Towards sustainable infrastructure materials: predictive physico-chemical modelling of concrete degradation

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  • Full or part time
    Dr R Myers
  • Application Deadline
    No more applications being accepted
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

The Industrial Ecology Team at the University of Edinburgh is seeking an outstanding PhD candidate to develop a new method that can predict the material and environmental performance of concrete infrastructure in service.

The economic cost of concrete corrosion is estimated to be on the order of single digit global GDP. Just under 10% of all anthropogenic CO2 emissions arise from the production of concrete. Concrete comprises 50% of all materials used other than water, significantly due to its use in infrastructure and because infrastructure materials like concrete facilitate high social well-being. The UK Government, facing urgent infrastructure challenges, is planning to invest £483bn into infrastructure by 2020/21. However, no method that can predict chemical degradation, environmental impacts, and mechanical performance of concrete currently exists.

These facts highlight the globally pervasive importance of concrete and the massive challenges that lie ahead in developing modern infrastructure materials. They also demonstrate the need for guidance informing which types of concrete have high holistic performance, and thus which concrete types should be used in modern infrastructure. Therefore, an exciting opportunity exists for a PhD candidate to develop a method that can provide this guidance. We believe that this method is one that can holistically predict concrete performance.

We are seeking a PhD candidate to develop and apply such a method. Building upon preliminary and ongoing work in the Industrial Ecology Team, the PhD candidate will be expected to work on some or all of the following lines of research:

Computational materials science: develop mathematical models/computational code/software, possibly involving Monte Carlo modelling and machine learning, to couple multi-scale physical and chemical properties of raw materials, cementitious binders, concrete, concrete degradation, and environmental impacts.

Materials chemistry: characterise physico-chemical properties of cementitious materials and especially those relating to concrete deterioration, develop new thermodynamic data for these materials, predict their occurrence using thermodynamic modelling.

Industrial ecology: beneficial use in cementitious systems (‘circular economy’, ’waste valorisation’, etc.), life cycle environmental impact assessment of cementitious materials.

This work aligns with an ongoing collaboration with fellow sustainable materials systems colleagues, e.g., at MIT, Yale University, and elsewhere. Therefore, we envisage that the PhD candidate will work on a highly transdisciplinary basis with experts across the University of Edinburgh and further afield, including internationally.

Essential criteria:
Applicants must hold undergraduate and/or masters degree in one or more of the following areas: engineering (e.g., chemical, materials, civil); science (e.g., mathematics, informatics, physics, chemistry, materials).
Demonstrable ability to perform mathematical modelling and/or coding.
Experimental/laboratory experience depending upon the PhD candidate’s area of interest.
High motivation to learn and work across traditional discipline boundaries.
Ability to work independently and in groups comprising people from diverse backgrounds.

Desirable criteria:
Basic coding experience (e.g., Python, MATLAB, R).

Start date flexible – applications will be considered on a rolling and individual basis. Applicants are encouraged to apply well before February 1st 2019 to meet the deadline for School of Engineering PhD scholarships beginning in 2019.

Funding Notes

Applications are welcomed from students who are applying for, or have been awarded, a scholarship or similar from the University of Edinburgh or elsewhere.

How good is research at University of Edinburgh in General Engineering?
(joint submission with Heriot-Watt University)

FTE Category A staff submitted: 91.80

Research output data provided by the Research Excellence Framework (REF)

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