PhD on Machine learning strategies for early prediction of structural embodied emissions

Sandie Kate Fenton successfully defended her PhD thesis titled “Machine learning strategies for early prediction of structural embodied emissions”, on Wednesday, 9 October 2024. In her research, Sandie developed an approach to predict and estimate total embodied greenhouse gas emissions in the early design phase, based on descriptive data such as structure typology and location.

Her research was supervised by Prof. Lars De Laet (VUB), Ass. Prof. Klaas De Rycke (Bollinger + Grohmann), and Prof. Gabriele Pierluisi (CY LéaV). The research also stronly benefited from the expertise of Prof. Adrian Munteanu (VUB). The defence appropriately took place at the 'Académie du Climat' in Paris. The external jury members were Prof. Sigrid Adriaenssens from Princeton University and Prof. Karla Saldaña Ochoa from the University of Florida.

More information about this research can be found in this publication. The full thesis will be made available online soon. For more information, please contact Lars De Laet.