Review on the Estimation of Static Deformability Modulus of Rocks and their adoptability in Different Rock Masses
Bibliographic record
Abstract
The aim of this study is to review the different mechanisms employed in the estimation of static rock mass deformability modulus (𝐸𝑚) in rock engineering applications and to investigate the adoptability of the identified mechanisms in different rock masses. The paper discusses different evaluation criteria through experimental, empirical and other means, with their merits and demerits, including influential factors. It is known that deformability modulus of intact rock depends on the imposed stress, strain rate and the confining stress on the rock sample as well as the rock texture and structure. The results generated for 𝐸𝑚 by different in-situ tests are different and an appropriate in-situ test based on the rock mass conditions should be employed to obtain reasonable results. Empirical criteria are found to produce results of reasonable precision if appropriately adopted for specific rock mass conditions, while the back analysis method is widely adopted as an insitu estimation measure for the design of rock-sockets and tunnel support. It has also been reported that substantial reduction in 𝐸𝑚 occurs due to schistosity and larger test volumes, while it is sensitive to stress and discontinuity conditions. In this work, specific recommendations are made on the estimation of 𝐸𝑚 for different types of rock masses based on the findings and reviews reported in the literature.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".