@misc{48a033d57e2548138d94f3852fe75887,
title = "Machine Learning for Advanced Building Construction",
abstract = "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.",
keywords = "build scans, building retrofits, exterior, machine learning, manufacturing",
author = "Hilary Egan and Clement Fouquet and Chioke Harris",
year = "2023",
language = "American English",
series = "Presented at the ICLR Tackling Climate Change with Machine Learning Workshop, 4 May 2023",
publisher = "National Renewable Energy Laboratory (NREL)",
address = "United States",
type = "Other",
}