LearnCarbon - A tool for machine learning prediction of global warming potential from abstract designs

Abstract

The new construction that is projected to take place between 2020 and 2040 plays a critical role in embodied carbon emissions. The change in material selection is inversely proportional to the budget as the project progresses. Given the fact that early-stage design processes often do not include environmental performance metrics, there is an opportunity to investigate a toolset that enables early-stage design processes to integrate this type of analysis into the preferred workflow of concept designers. The value here is that early-stage environmental feedback can inform the crucial decisions that are made in the beginning, giving a greater chance for a building with better environmental performance in terms of its life cycle. This paper presents the development of a tool called LearnCarbon, as a plugin of Rhino3d, used to educate architects and engineers in the early stages about the environmental impact of their design. It facilitates two neural networks trained with the Embodied Carbon Benchmark Study by Carbon Leadership Forum, which learns the relationship between building geometry, typology, and construction type with the Global Warming potential (GWP) in tons of C02 equivalent (tCO2e). The first one, a regression model, can predict the GWP based on the massing model of a building, along with information about typology and location. The second one, a classification model, predicts the construction type given a massing model and target GWP. LearnCarbon can help improve the building life cycle impact significantly through early predictions of the structure's material and can be used as a tool for facilitating sustainable discussions between the architect and the client.

Speculative hybrids: Investigating the generation of conceptual architectural forms through the use of 3D generative adversarial networks

“…This fact may be due to perceived and existing limitations of computational design tools (they encode architectural qualities into quantitative metrics), because of their inaccessibility (computational design requires skills that not all of us have), or due to the cost of their implementation (using technology is sometimes more complicated than doing the [initial] design by yourself). The ones that use computational design tools utilize them to simulate how a building will perform and try to fit environmental sustainability metrics using, for example, Ladybug (Roudsari et al, 2013) 77 or LearnCarbon (Kharbanda et al, 2022). 78 Most participants noted that using computational design tools involves forcing oneself to think in ways that are too rigid or limiting and that computational tools involve no socio-spatial considerations.…”

“…The ones that use computational design tools utilize them to simulate how a building will perform and try to fit environmental sustainability metrics using, for example, Ladybug (Roudsari et al, 2013) 77 or LearnCarbon (Kharbanda et al, 2022). 78 Most participants noted that using computational design tools involves forcing oneself to think in ways that are too rigid or limiting and that computational tools involve no socio-spatial considerations. These early stages of architectural design include decisions that, as one of our participants mentioned need to be taken out of the algorithm.…”