Rock quality designation (RQD): time to rest in peace
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rock quality designation (RQD) was introduced by Don Deere in the mid-1960s as a means of using diamond core to classify rock for engineering purposes. Subsequently, it was incorporated into the rock mass rating (RMR) and Q-system classification methods that, worldwide, now play substantial roles in rock mechanics design, whether for tunnels, foundations, rock slopes or rock excavation. It is shown that a key facet of the definition of RQD is ignored in many parts of the world, and it is noted that there are several inherent limitations to the use of RQD. Based on mapping of rock formations by 17 independent professionals at different locations in Australia and South Africa, it is shown that differences in assessed RQD values result in significant errors in computed RMR and Q ratings, and also in geological strength index (GSI) and mining rock mass rating (MRMR). The introduction of a look-up chart for assessing GSI has effectively removed the need to measure, or estimate, RQD. It has been found that GSI values derived from the look-up chart are as valid as those derived by calculation from the original component parameters, and are satisfactorily consistent between professionals from diverse backgrounds. The look-up charts provide a quick and appropriate means of assessing GSI from exposures. GSI is, in turn, a useful rock mass strength index; one new application is presented for assessing potential erosion of unlined spillways in rock. Incorporation of RQD within the RMR and Q classification systems was a matter of historical development, and its incorporation into rock mass classifications is no longer necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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