Machine Learning for Advanced Building Construction: Preprint

Hilary Egan, Clement Fouquet, Chioke Harris

Research output: Contribution to conferencePaper


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
Number of pages9
StatePublished - 2023
EventICLR - Virtual
Duration: 1 May 20235 May 2023



NREL Publication Number

  • NREL/CP-2C00-85804


  • building retrofits
  • machine learning


Dive into the research topics of 'Machine Learning for Advanced Building Construction: Preprint'. Together they form a unique fingerprint.

Cite this