Prediction of end-bearing capacity of rock-socketed shafts considering rock quality designation (RQD)
Bibliographic record
Abstract
Existing empirical methods for determining the end-bearing capacity, qmax, use empirical relations between qmax and the unconfined compressive strength of intact rock, σc. As rock-socketed shafts are supported by the rock mass, not just the intact rock, one should consider not only the intact rock properties, but also the influence of discontinuities on rock mass properties when determining qmax. Although semi-empirical and analytical methods have been developed that can consider the effect of discontinuities, they are more complicated than the empirical relations and require information about discontinuities that is often not available or difficult to obtain in engineering practice. In this paper, an empirical relation between qmax and the unconfined compressive strength of rock mass, σcm, is developed. The new empirical relation explicitly considers the effect of discontinuities represented by rock quality designation (RQD), which is the parameter normally obtained in engineering practice. The accuracy of the expression for estimating σcm based on RQD is verified by comparing its estimation values with those from the existing empirical expressions based on rock mass classification. Two examples are presented to show the application of the newly developed empirical relation between qmax and σcm.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".