All Outputs (2)

Machine Learning for Sustainable Structures: A Call for Data (2018)
Journal Article
D'Amico, B., Myers, R., Sykes, J., Voss, E., Cousins-Jenvey, B., Fawcett, W., …Pomponi, F. (2019). Machine Learning for Sustainable Structures: A Call for Data. Structures, 19, 1-4. https://doi.org/10.1016/j.istruc.2018.11.013

Buildings are the world's largest contributors to energy demand, greenhouse gases (GHG) emissions, resource consumption and waste generation. An unmissable opportunity exists to tackle climate change, global warming, and resource scarcity by rethinki... Read More about Machine Learning for Sustainable Structures: A Call for Data.

Accuracy and reliability: a computational tool to minimise steel mass and carbon emissions at early-stage structural design (2018)
Journal Article
D’Amico, B., & Pomponi, F. (2018). Accuracy and reliability: a computational tool to minimise steel mass and carbon emissions at early-stage structural design. Energy and Buildings, 168, 236-250. https://doi.org/10.1016/j.enbuild.2018.03.031

Building structures often represent the element with the largest mass in a building project, with significant effects on the buildings life cycle environmental impacts. Amongst structural materials, steel is characterised by its suitability to a larg... Read More about Accuracy and reliability: a computational tool to minimise steel mass and carbon emissions at early-stage structural design.