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Bentonite mass loss in fractured crystalline rock quantified from CT scans using digital rock physics and machine learning: case study from the Grimsel Test Site (Switzerland)

Sadeghnejad, Saeid; Hupfer, Sarah; Pingel, Janis; Lanyon, Bill; Schneeberger, Raphael; Blechschmidt, Ingo; Alonso, Ursula; Hauser, Wolfgang 1; Kraft, Stephanie 1; Geckeis, Horst 1; Schäfer, Thorsten
1 Institut für Nukleare Entsorgung (INE), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

Bentonite plays a critical role in engineered barrier systems designed for radioactive waste storage in geological repositories especially in crystalline formations. Ensuring its long-term stability under realistic hydrogeochemical conditions is vital for evaluating the safety of these repositories. This study investigated the influence of controlled water flow in a shear zone on the erosion of bentonite through a 4.5-year Long-Term In-Situ Test (LIT) at the Grimsel Test Site, Switzerland. Compacted Ca-Mg-type FEBEX bentonite rings (with 90 % montmorillonite content) were positioned in-situ in an emplacement borehole intersecting a water-conducting shear zone providing direct contact with low-mineralized glacial meltwater. X-ray computed tomography scanning, along with digital rock physics methods, were used to quantify bentonite mass loss and the contact shear zone aperture distribution on over-cored LIT samples. A Random Forest classifier, a machine learning technique, was used for segmentation, which enabled more precise quantification of bentonite mass loss and improved fault characterization. This approach used multiphase segmentation, allowing accurate distinction between different material phases in the cored interval, which is essential for resolving complex interactions in heterogeneous systems. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000188982
Veröffentlicht am 18.12.2025
Originalveröffentlichung
DOI: 10.1016/j.clay.2025.107915
Scopus
Zitationen: 1
Web of Science
Zitationen: 1
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Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Nukleare Entsorgung (INE)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 11.2025
Sprache Englisch
Identifikator ISSN: 0169-1317
KITopen-ID: 1000188982
HGF-Programm 32.11.01 (POF IV, LK 01) Nuclear Waste Disposal
Erschienen in Applied Clay Science
Verlag Elsevier
Band 276
Seiten 107915
Schlagwörter Fracture aperture distribution, Bentonite erosion, FEBEX bentonite, CT scan, Digital twin, Machine learning, Uncertainty analysis
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