Machine learning applied to Material Flow Analysis - Journal of Industrial ecology spotlight

babak-ebrahimi, Leonardo Rosado, Holger Wallbaum
Last updated on Oct 10, 2022 1 min read

Material flows in and out of road structures and pavements between 1980 and 2017. Underlying data for Figure 3 are available in a table entitled “Figure 3” of Supporting Information S2
The Journal of Industrial Ecology shared the article “Machine learning-based stocks and flows modeling of road infrastructure” by Babak Ebrahimi, Leonardo Rosado and Holger Wallbaum. The paper introduces a new method using machine learning applied to material flow analysis as an alternative to achetype modeling to study road infrastructure. Holger Wallbaum says this was the first attempt to apply machine learning based methods to MFA but is confident in developing this method further with SB’s newest colleague, Maud Lanau .

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Link to research paper
Link to Post

Bottom-up-modeling dynamic-modeling machine-learning material-flow-analysis MFA
Leonardo Rosado
Leonardo Rosado
Associate Professor

Studying cities from an Urban metabolism perspective. Its flows and stocks, its functions and needs. To provide information towards urban planning and circular economy.

Holger Wallbaum
Holger Wallbaum
Full Professor, Vice-Head of Department and Vice-Dean for Research

Holger is a Full Professor in sustainable building at the Division of Building Technology, research group Sustainable Building, and in the Area of advance Building Futures. Holger works within sustainable building on concepts, tools and strategies to enhance the sustainability performance of construction materials, building products, buildings as well as entire cities.