Reliability assessment of deep excavations in spatially random cohesion weakening friction strengthening massive rocks: Application to nuclear repositories
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Bibliographic record
Abstract
An augmented methodology is developed to estimate the reliability of deep excavations along spatially variable massive rock masses using the Cohesion Weakening Friction Strengthening (CWFS) model. Sensitive parameters of the CWFS model were initially identified using Sobol’s global sensitivity analysis based on their influence on the displacements and excavation damage zone around excavations. The probability of failure was estimated by performing Mont–Carlo Simulations on random finite difference models of excavations generated via MATLAB-FLAC2D coupling, considering the spatial variation of these sensitive parameters. Spatial variation was modeled by generating anisotropic random fields of sensitive CWFS parameters via the recently developed Fourier series method and updated correlations suggested by Walton (2019). The proposed methodology was demonstrated for a proposed deep nuclear waste repository to be located in Canada. Results from the developed methodology were systematically compared with those of traditional reliability (ignoring spatial variation) and deterministic methods (ignoring uncertainty). Although the developed methodology was computationally complex, it was judged to be the most realistic due to the realistic consideration of heterogeneous distributions of rock properties. Traditional methodologies underestimate/overestimate the excavation performance due to negligence of uncertainty and spatial variability. Finally, a parametric analysis was performed using developed methodology by varying the initial friction angle, scale of fluctuations (SOFs) and dilation angle. The effect of initial friction angle was observed to be more pronounced on the probability of failures as compared to SOFs and dilation angle. Similar observations were made related to the EDZ development quantified using yield area ratio.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it