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Record W4411642655 · doi:10.1016/j.clay.2025.107915

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)

2025· article· en· W4411642655 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Clay Science · 2025
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsFractal Systems (Canada)
FundersBundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und VerbraucherschutzNationale Genossenschaft für die Lagerung radioaktiver Abfälle
KeywordsBentoniteGeologyTest siteRock mass classificationMineralogyGeotechnical engineeringMining engineering

Abstract

fetched live from OpenAlex

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. The selection of the correct region of interest was crucial for minimizing segmentation errors and improving mass loss quantification by reducing interferences from non-relevant structures. The aperture distribution between the three boreholes over-cored within the shear zone was evaluated with a mean thickness of 2.90 ± 1.09 mm (2σ). Furthermore, the bentonite mass loss was computed from the scanned images and compared with mobilised montmorillonite colloid masses, continuously sampled in the water from observation boreholes (0.11–0.12 m and 6 m distance) measured by inductively coupled plasma mass spectrometry (ICP-MS) and laser-induced breakdown detection (LIBD) techniques. The data evaluation of both techniques used in this study provided erosion rates <2 kg/m 2 /y, which are at least two orders of magnitude below the mass loss assessment rates of 500 to 1500 kg/m 2 /y defined by safety case considerations of the Swedish Nuclear Fuel and Waste Management Company (Svensk Kärnbränslehantering Aktiebolag, SKB) and the Finnish company POSIVA handling the final disposal of the spent nuclear fuel generated by its owners, the nuclear plant operators Teollisuuden Voima and Fortum. The creation of a digital twin model for the bentonite-water-shear zone system provided new insights into the erosion processes showing inhomogeneous erosion in contact with real fracture geometries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.247
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it