Validation of a Continuum-based Weak Zone Joint Model for Simulating Anisotropic Rock Mass Strength
Bibliographic record
Abstract
Abstract In recent decades, the approach to estimating rock mass strength and its anisotropic behaviour more accurately has been a growing topic of interest in the rock mechanics community. To evaluate jointed rock mass behaviour more accurately, sophisticated modelling techniques such as Synthetic Rock Mass (SRM) models are becoming increasingly popular. Still, these methods are computationally intensive and require detailed site characterization data that are often unavailable during the initial stages of project development and are not always the most practical option. This study developed an alternative continuum-based SRM-like model where thin contiguous regions of weak continuum material with equivalent joint properties are in place of explicit interface joint elements. The finite-difference program FLAC used these weak zone joints (WZJ) to generate a jointed rock mass continuum model (JRCM). The JRCM concept was applied to systematic sets of persistent joints, a single non-persistent joint, and two intersecting joints to validate the practicality of using WZJ. Results found that the concept produced reasonable anisotropic rock mass strength estimates for most cases, with some limitations. The model captured the anisotropic behaviour while reducing time requirements to develop and recalibrate a conventional SRM. The JRCM concept demonstrates a practical alternative to estimating anisotropic rock mass strength for specific projects where the quantity or quality of data is limited. Furthermore, the reduced run times could allow multiple iterations to perform a probabilistic assessment of the rock mass strength.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".