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 cm2 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.