TY  - CONF
A1  - Zia, Ghezal Ahmad Jan
A1  - Völker, Christoph
A1  - Moreno Torres, Benjami
A1  - Kruschwitz, Sabine
T1  - An Adaptive Upscaling Approach for Assessing Materials’ Circularity Potential with Non-destructive Testing (NDT)
T2  - Proceedings of the RILEM Spring Convention and Conference 2024
N2  - 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.
T2  - Rilem Spring Convention
CY  - Milano, Italy
DA  - 09.04.2024
KW  - Adaptive Sampling
KW  - Random Sampling
KW  - Machine Learning
KW  - Non-Destructive Testing
KW  - Condition Assessment
KW  - Circular Economy
PY  - 2024
SN  - 978-3-031-70277-8
DO  - https://doi.org/10.1007/978-3-031-70277-8_38
SN  - 2211-0844
VL  - 55
SP  - 330
EP  - 338
PB  - Springer Nature Switzerland
CY  - Switzerland
AN  - OPUS4-62458
LA  - eng
AD  - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER  -