Predicting embodied carbon building structure is a M.Tech project topic for Environmental Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Predicting embodied carbon building structure Project Details
| Abstract |
The building sector accounts for about 38 % of global CO₂ emissions tied to energy use. A large share of that comes from embodied carbon in structural systems, which is released during material production. Traditional policies have mainly aimed at making buildings use less energy while they operate, but cutting embodied carbon needs actions early in the design process, when the choice of structural material is made. Measuring embodied carbon quickly and accurately is hard because material sources differ by region and there are few easy assessment tools. This project outlines research directions and practical steps for building machine‑learning models that can predict the embodied carbon of different structural types.
The approach uses past structural design databases, material quantities, and regional carbon factors to test how well supervised‑learning algorithms perform. The goal is to create a reliable baseline that engineers can use early in the design stage, so they can pick materials that lower greenhouse‑gas emissions before detailed volume modeling. The guidance also helps set up predictive workflows that link structural design decisions to environmental impact assessments.
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| Reference Paper |
Predicting embodied carbon of building structure types through Machine Learning |
| Domain |
Environmental Engineering |
| Sub-Domain |
Sustainable Systems / Climate & Sustainability / Carbon Footprint |
| PDF Download |
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| Get Help |
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