Digital tool integrations for architectural reuse of salvaged building materials

Building material reuse can reduce the environmental impact of construction yet its advanced digital support is still limited. Which digital tools could effectively support repair of highly irregular, salvaged materials? To probe this question, a framework featuring six advanced digital tools is proposed and verified through six design and prototyping experiments. The experiments demonstrate that a digital toolkit integrating photogrammetry, robot vision, machine learning, computer vision, computational design, and robotic 3D printing effectively supports repair and recovery of irregular reclaimed materials, enabling their robust digitization, damage detection, and feature-informed computational redesign and refabrication. These findings contribute to the advancement of digitally aided reuse practices in the construction sector, providing valuable insights into accommodating highly heterogeneous reclaimed materials by leveraging advanced automation and digitization. They provide the crucial and currently missing technological and methodological foundation needed to inform future research on industrial digital solutions for reuse. • Digital tool integration aiding reuse of reclaimed building materials is presented. • Photogrammetry, robot vision, ML, computer vision, and 3D printing are combined. • Data-driven robot path generation is demonstrated. • Six design scenarios for digitally aided salvaged material reuse are shown. • Pros and cons of data capture as images, point clouds, and meshes are discussed.