Validation of empirical rock mass classification systems for rock slopes
Bibliographic record
Abstract
ABSTRACT Many classification systems have been proposed in the literature to identify the state of stability of rock slopes. Most of these classification systems involve factors relevant to the general condition of the rock mass, for example, intact rock strength (UCS), geometry and condition of discontinuities, and groundwater condition. Such factors represent the basic part of most of the classification systems, which refer to the well-known Bieniawski’s Rock Mass Rating or RMR system. However, these factors were initially developed for underground excavations. Therefore, these classification systems have been subjected to many criticisms and were questioned for their suitability for rock slopes. In this paper, some of the common classification systems for rock slopes are used to identify their suitability for rock cuts. Twenty two sites of rock cuts in mountainous roads affected by heavy rainfall in the southwestern part of Saudi Arabia have been selected as case studies, and four empirical methods are examined for these case studies. The selected methods are Slope Mass Rating or SMR (Romana, 1985), continuous SMR (Tomas, 2007), Chinese SMR (Chen, 1995), and a graphical SMR (Romana, 2012). The stability conditions for each site have been determined by each of these methods and a comparison between the results is made for the case of plane failure mode. It is shown that some of the empirical methods are not applicable such as Chinese SMR (for slopes less than 80 m high), and the graphical SMR method when the slope angle is more than 80°.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".