An Adaptive Upscaling Approach for Assessing Materials’ Circularity Potential with Non-destructive Testing (NDT)

  • 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. TheAdvancing 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.zeige mehrzeige weniger

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Autor*innen:Ghezal Ahmad Jan ZiaORCiD, Christoph Völker, Benjami Moreno TorresORCiD, Sabine KruschwitzORCiD
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Proceedings of the RILEM Spring Convention and Conference 2024
Jahr der Erstveröffentlichung:2024
Organisationseinheit der BAM:8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.0 Abteilungsleitung und andere
Verlag:Springer Nature Switzerland
Verlagsort:Switzerland
Jahrgang/Band:55
Erste Seite:330
Letzte Seite:338
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten
Freie Schlagwörter:Adaptive Sampling; Circular Economy; Condition Assessment; Machine Learning; Non-Destructive Testing; Random Sampling
Themenfelder/Aktivitätsfelder der BAM:Material
Material / Additive Fertigung
Veranstaltung:Rilem Spring Convention
Veranstaltungsort:Milano, Italy
Beginndatum der Veranstaltung:09.04.2024
Enddatum der Veranstaltung:12.04.2024
DOI:10.1007/978-3-031-70277-8_38
ISSN:2211-0844
ISBN:978-3-031-70277-8
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:29.01.2025
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:05.05.2025
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