Print Email Facebook Twitter Automating building element detection for deconstruction planning and material reuse Title Automating building element detection for deconstruction planning and material reuse: A case study Author Gordon, Matthew (ETH Zürich; Institute for Advanced Architecture of Catalonia) Batallé Garcia, A. (TU Delft Design & Construction Management) De Wolf, Catherine (ETH Zürich) Sollazzo, Aldo (Institute for Advanced Architecture of Catalonia) Dubor, Alexandre (Institute for Advanced Architecture of Catalonia) Wang, T. (TU Delft Design & Construction Management) Date 2023 Abstract To address the need for a shift from a linear to a circular economy in the built environment, this paper develops a semi-automated assistive process for planning building material deconstruction for reuse using sensing and scanning, Scan-to-BIM, and computer vision techniques. These methods are applied and tested in a real-world case study in Geneva, Switzerland, with a focus on reconstruction and recovery analysis for floor beam systems. First, accessible sensing and scanning tools, such as mobile photography and smartphone-based consumer-grade Lidar devices, are used to capture imagery and other data from an active demolition site. Then, photogrammetry and point cloud data analysis are performed to construct a 3D BIM model of relevant areas. The structural relationships between reconstructed BIM elements are evaluated to score the feasibility for recovery of each element. This study illustrates what is feasible and where further development is necessary for automating building material reuse planning at scale to increase the uptake of circular economy practices in the construction sector. Subject BIMBuilding deconstructionCircularityDigitalizationLidarMaterial reusePhotogrammetryPoint cloud To reference this document use: http://resolver.tudelft.nl/uuid:54db0f5c-6a06-4e95-b4d9-28a843615aca DOI https://doi.org/10.1016/j.autcon.2022.104697 ISSN 0926-5805 Source Automation in Construction, 146 Part of collection Institutional Repository Document type journal article Rights © 2023 Matthew Gordon, A. Batallé Garcia, Catherine De Wolf, Aldo Sollazzo, Alexandre Dubor, T. Wang Files PDF 1_s2.0_S0926580522005672_main.pdf 10.68 MB Close viewer /islandora/object/uuid:54db0f5c-6a06-4e95-b4d9-28a843615aca/datastream/OBJ/view