Lifecycle-Long Environmental Impacts of Supplementary Cementitious Materials (SCMs) in Sustainable Land Regeneration: A Machine-Learning Based Approac

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This PhD work aims to establish a machine-learning (ML) based prediction model for assessing lifecycle-long environmental impacts of current SCMs available in land regeneration, with a special focus on stabilisation/solidification (S/S) technology. The work will include a state-of-art review on lifecycle assessments (LCAs) of current SCMs, then build up a database as well as develop a novel framework for performing LCAs for S/S project. The database and framework will then formulate foundations of the ML based prediction model to better understand the environmental impacts of SCMs systematically, in order to further promote sustainability of S/S technique and project added values of land remediation project.