An opportunity has arisen for an outstanding Ph.D. student to join the industrial ecology team at the University of Edinburgh to work on Data driven innovation of sustainable infrastructure materials.
Concrete is the second-most used material on Earth, and hydrated cement, its key binding phase, alone contributes to around 7% of all anthropogenic CO2 emissions. Population growth will cause such materials to be used in greater amounts in the future than ever before; therefore, their associated environmental impacts will almost certainly increase too. This scenario may, however, be avoided if significant engineering performance and environmental improvements are made to cement related materials (CRMs) technology and implemented in practice.
Deterministic experimental and modelling approaches to understanding cement and concrete properties have greatly advanced over many years. Presently, the physico-chemical properties of high Portland cement (PC) materials are generally well known, as they also are for an increasing number of low(er) PC materials involving common supplementary cementitious materials like fly ash. However, low(er) PC materials with increasingly diverse chemistry and performance continue to be developed in an effort to reduce CO2 emissions, which is increasing the number of viable CRMs available for use. In turn, a growing number of physico-chemical processes need to be better understood in order to analyse their performance, making applications of deterministic approaches more challenging. Similarly, it is becoming more challenging to assess their life cycle environmental impacts (hereafter ‘impacts’) due to the growing number of viable CRMs. Such assessments require coupling between material chemistry, performance (e.g., durability), and application (e.g., provision of an amount of load bearing capacity to a building over its lifespan) to be considered.
This Ph.D. project will develop probabilistic algorithms, involving Monte Carlo modelling and machine learning, that will be used to explore relationships among key chemical and physical properties (e.g., durability with respect to steel corrosion, and binder porosity), and also impacts of CRMs. They will use realistic properties and assumptions about the behaviour of CRMs, and integrate thermodynamic modelling, understanding of corrosion chemistry, and life cycle assessment.
It is envisaged that the algorithms will be developed by validation with respect to already standardised materials initially, such as those in BS EN 197-1:2011, and later to novel materials. This may exceptionally involve experimental synthesis and characterisation of such materials, for example, the development of solubility data for solid phases such as those present in alkali-activated materials.
Outcomes of the Ph.D. project will include the identification of parameter spaces for CRMs that approach optimal performance. Performance may be specified in terms of key chemical and physical properties (e.g., durability, compressive strength), and impacts (e.g., global warming potential). These results may, for example, be visualised in ternary diagrams. It is envisaged that results from this Ph.D. project will thus guide the development, optimisation, and use of higher performance and lower impact CRMs.
We envisage that the Ph.D. student will work on a highly transdisciplinary basis with experts across the School of Engineering. We also envisage that there will be opportunities for the Ph.D. student to collaborate with researchers based elsewhere in the UK and internationally.
Flexible – applications will be considered on a rolling and individual basis. Applicants are encouraged to apply well before 1st February 2019 to meet the deadline for School of Engineering PhD scholarships beginning in 2019.
Applicants must hold an undergraduate and/or masters degree in one or more of the following areas or a related discipline: engineering (e.g., chemical, materials, civil); science (e.g., mathematics, informatics, physics, chemistry, materials, environmental science, forestry).
A demonstrable ability to code and/or coding experience (e.g., in Python, MATLAB, R)
Experience in statistical data analysis, including regressions.
A high motivation to learn and work across traditional discipline boundaries.
Ability to work independently and in groups comprising people from diverse backgrounds.
Further information on English language requirements for EU/Overseas applicants.
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.