Intelligent Carbon Footprint Prediction from Architectural Design Visuals

DossierGOCI.KIEM.02.017
StatusLopend
Startdatum1 januari 2023
Einddatum30 september 2023
RegelingKIEM GoCI 2020-2023
Thema's
  • B├Ętatechniek
  • Energie en Klimaat - Een volledig CO2-vrij elektriciteitssysteem in 2050
  • Energietransitie en duurzaamheid
  • Gebouwde omgeving duurzaam en leefbaar

This project assists architects and engineers to validate their strategies and methods, respectively, toward a sustainable design practice. The aim is to develop prototype intelligent tools to forecast the carbon footprint of a building in the initial design process given the visual representations of space layout.
The prediction of carbon emission (both embodied and operational) in the primary stages of architectural design, can have a long-lasting impact on the carbon footprint of a building. In the current design strategy, emission measures are considered only at the final phase of the design process once major parameters of space configuration such as volume, compactness, envelope, and materials are fixed. The emission assessment only at the final phase of the building design is due to the costly and inefficient interaction between the architect and the consultant. This proposal offers a method to automate the exchange between the designer and the engineer using a computer vision tool that reads the architectural drawings and estimates the carbon emission at each design iteration. The tool is directly used by the designer to track the effectiveness of every design choice on emission score. In turn, the engineering firm adapts the tool to calculate the emission for a future building directly from visual models such as shared Revit documents.
The building realization is predominantly visual at the early design stages. Thus, computer vision is a promising technology to infer visual attributes, from architectural drawings, to calculate the carbon footprint of the building. The data collection for training and evaluation of the computer vision model and machine learning framework is the main challenge of the project. Our consortium provides the required resources and expertise to develop trustworthy data for predicting emission scores directly from architectural drawings.

Contactinformatie

TU Delft

Seyran Khademi, contactpersoon
Telefoon: 06-81133375

Consortiumpartners

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