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Record W4412194199 · doi:10.5194/safend2025-108

Quality Assurance of Bentonite - Prospects for Quality Assurance of Bentonite as a Geotechnical Barrier

2025· preprint· en· W4412194199 on OpenAlexaboutno aff
Wolf Andreas Dr. Schmidt

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsBentoniteQuality assuranceGeologyGeotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

The quality assurance of bentonite as a geotechnical barrier is crucial for the safe final disposal of radioactive waste. According to Paragraph 4, Section 3, No. 2 of the EndlSiAnfV [1], the geotechnical barrier, in combination with the technical barrier, forms a key component of the safety system in crystalline host rock. Together, these barriers must ensure the safe confinement of radioactive waste over an assessment period of one million years.As outlined in Paragraph 6, Section 4 of the EndlSiAnfV, the quality assurance process for manufacturing and constructing essential technical and geotechnical barriers must adhere to the state of the art in science and technology. The properties of the geotechnical barrier must be defined in the repository's safety concept.The performance of bentonite as a geotechnical barrier is primarily influenced by the raw material used and its processing (e.g., mineral additives, shaping, and compaction). Quality assurance must establish criteria for each step of the production process and ensure compliance with these standards. It is advisable to define acceptable ranges for these quality criteria. For example, material parameters such as swelling pressure or sulfur mineral content, as specified in the safety concept, may vary within a permissible range. Any deviations must be addressed with corrective measures, which should be clearly defined as part of the quality assurance process.Bentonite, as a natural mineral, is mined from various deposits worldwide [2]. These deposits can vary significantly in their mineralogical composition, such as smectite content, sulfur mineral content, and iron mineral content. Key properties of bentonite required for safe final storage include swelling pressure [3], Eh value [4], and corrosive potential [5]. Therefore, quality assurance must begin with the mining of bentonite to ensure that the necessary measures are taken at each stage of the production process to maintain the target parameters within acceptable limits.In Germany, repository concepts consider bentonite as geotechnical barrier in the near field as well as in sealing constructions. A large quantity of bentonite will be required as a geotechnical component. The potential limited global availability of bentonite from different deposits should already be factored into the quality assurance strategy.Literature[1] Endlagersicherheitsanforderungsverordnung (EndlSiAnfV) vom 6. Oktober 2020 (BGBl. I S. 2094)[2] Svensson, Daniel et. al. (2017): Developing strategies for acquisition and control of bentonite for a high level radioactive waste repository. Svensk Kärnbränslehantering AB (SKB)[3] Dixon, David. A. (2019): Review of the T-H-M-C Properties of MX-80 Bentonite. NWMO. Toronto (NWMO-TR-2019-07)[4] Posiva (2021): Safety Case for the Operating Licence Application - Models and Data (M&D) POSIVA 2021-04. Posiva Oy[5] Behazin, Mehran et. al. (2021): State of Science Review of Sulfide Production in Deep Geological Repositories for Used Nuclear Fuel. Nuclear Waste Management Organization. Toronto (NWMO-TR-2021-18)

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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.315
Teacher spread0.291 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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