Enabling Circular Reuse of Sandwich Panels Through UAV Inspection, Deep Learning, and BIM-Based Material Passports

Transitioning toward a circular economy requires not only solutions involving technical component reuse but also mechanisms that reduce risk and increase confidence among market stakeholders. Steel-faced sandwich panels, widely used in façades and roofs, constitute a significant urban material stock, yet their reuse is constrained by information asymmetry, liability concerns, and the absence of verifiable condition data. In this study, we develop an integrated end-to-end workflow—combining controlled panel recovery, Unmanned Aerial Vehicle (UAV) inspection, deep learning-driven damage detection, and Building Information Modeling (BIM)-linked material passports—to enable traceable, evidence-based reuse decisions. Validated through a pilot façade assembly and disassembly process, the methodology successfully quantified 4845.90 cm 2 of mechanical damage across 10 panels, with all orthomosaic and detection outputs fully integrated into the digital passport environment. By standardizing component-level condition records, this approach reduces perceived risk and provides the technical assurance necessary to unlock a trusted second-hand marketplace for sandwich panels. Framed within an urban metabolism perspective, the findings demonstrate how digital transparency can bridge the gap between material recovery and market valuation.