Machine Learning for Advanced Building Construction

Hilary Egan, Clement Fouquet, Chioke Harris

Research output: NRELPoster


High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)-enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.
Original languageAmerican English
PublisherNational Renewable Energy Laboratory (NREL)
StatePublished - 2023

Publication series

NamePresented at the ICLR Tackling Climate Change with Machine Learning Workshop, 4 May 2023

NREL Publication Number

  • NREL/PO-2C00-85803


  • build scans
  • building retrofits
  • exterior
  • machine learning
  • manufacturing


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