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of concrete performance, particularly long-term performance, is an obstacle to the adoption and scaling of novel concrete mixes. This project investigates how to better predict future concrete performance
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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
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. This PhD will utilise thermodynamic modelling to obtain predictions of equilibrium and, where possible, non-equilibrium phases for a matrix of compositions covering flat rolled products including elevated
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