62458 2024 eng 330 338 55 conferenceobject Springer Nature Switzerland Switzerland 1 -- -- -- Advancing towards a circular economy necessitates the efficient reuse and maintenance of structural materials, which relies on accurate, non-damaging condition assessments. This paper introduces an innovative AI-driven adaptive sampling (AS) technique integrated with Non-Destructive Testing (NDT) to optimize this process. AS focuses on critical data points, reducing the amount of data needed for precise assessments—evidenced by our method requiring on average only 7 samples for Logistic Regression and 8 for Random Forest, contrasted with 29 for traditional sampling. By reducing the necessity for extensive data collection, our method not only streamlines the assessment process but also significantly contributes to the sustainability goals of the circular economy. These goals include resource efficiency, waste reduction, and material reuse. Efficient condition assessments promote infrastructure longevity, reducing the need for new materials and the associated environmental impact. The circular economy aims to create a sustainable system where resources are reused, and waste is minimized. This is achieved by extending the lifecycle of materials, reducing the environmental footprint, and promoting recycling and reuse. Longevity directly contributes to the circular economy by maximizing the utility and lifespan of existing materials and structures. Longer-lasting infrastructure means fewer resources are needed for repairs or replacements, leading to reduced material consumption and waste generation. This aligns with the circular economy's principles of sustainability and resource efficiency. This research not only advances the field of structural health monitoring but also aligns with the broader objective of enhancing sustainable construction practices within the circular economy framework. Proceedings of the RILEM Spring Convention and Conference 2024 2211-0844 978-3-031-70277-8 10.1007/978-3-031-70277-8_38 Rilem Spring Convention Milano, Italy 09.04.2024 12.04.2024 publish 05.05.2025 Ghezal Ahmad Jan Zia Christoph Völker Benjami Moreno Torres Sabine Kruschwitz eng uncontrolled Adaptive Sampling eng uncontrolled Random Sampling eng uncontrolled Machine Learning eng uncontrolled Non-Destructive Testing eng uncontrolled Condition Assessment eng uncontrolled Circular Economy Ingenieurwissenschaften und zugeordnete Tätigkeiten 8 Zerstörungsfreie Prüfung Material Verlagsliteratur Datei im Netzwerk der BAM verfügbar ("Closed Access") 8.0 Abteilungsleitung und andere Additive Fertigung