Application of rock mass classification techniques to weak rock masses: A case study from the Ruahine Range, North Island, New Zealand
Bibliographic record
Abstract
Rock mass classification systems were first generated for use in engineering applications, but their potential for utilization in geomorphic studies has since been recognized. These techniques were mostly developed for hard rock environments, and questions remain about their applicability to weak rocks. Here, the applicability of three rock mass classification techniques (rock mass strength (RMS), rock mass rating (RMR), and slope mass rating (SMR)) to weak rock masses was analyzed. Techniques incorporated parameters such as uniaxial compressive rock strength, discontinuity condition and orientation, and groundwater ratings. Rock mass classification values were determined from 14 profiles sited on recently excavated road cuttings on the Saddle Road, in the Ruahine Range, North Island, New Zealand. This is an important transport route across the North Island’s axial ranges, with the road excavated into weak late Pliocene – Early Pleistocene mudstone. Mean slope and minimum slope angle were measured at each profile in concert with the rock mass classification schemes. The three classification techniques all appear to have limited usefulness given the subaerial conditions prevalent at the study site. It would appear that the relative weightings of the different parameters within the RMR, RMS, and SMR classification schemes would need modifying for weak rock masses, but the precise details of this are difficult to determine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".