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)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".