Advancements in rock block volume calculation by analytical method for geological engineering applications
Bibliographic record
Abstract
Abstract The shape, the volume, and the distribution of the rock blocks represent important geomechanical factors of a rock mass behavior in engineering works. Several methods have been developed for estimating these parameters, including numerical models, as well as analytical and empirical methods. However, their determination in actual in-situ conditions can be quite challenging. The existing analytical methods show limitations in determining the in-situ rock blocks volume. Numerical models provide more reliable estimates of these parameters, but they are not accessible to all, and they require a good working knowledge. Increasing the accuracy of existing analytical methods, or developing more reliable and accessible methods, are more realistic approaches to obtain better estimates of rock block volumes. This paper presents a new method to obtain more accurate estimates of in-situ rock block volume. The method is developed for rock a mass consisting of three persistent joint sets, each set having constant spacing and orientation values. It is based on vector products to obtain exact block volumes, an improvement as compared to previous methods. The volumes of the rock blocks are calculated through the multiplication of the blocks’ edge vector. The results of the developed equation are validated with the output of numerical simulations using 3DEC version 7.0 software, and the results indicate that the developed method makes it possible to determine in-situ rock block volume more reliably than the existing methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".