Determination of joint roughness coefficient (JRC) for slope stability analysis: a case study from the Gold Coast area, Australia
Bibliographic record
Abstract
Surface roughness of rock discontinuities is an important factor that determines the strength characteristics of rock mass. Joint roughness coefficient (JRC), which is typically measured by means of Barton’s combs in the field, is widely used to describe the joint roughness. However, this traditional method of measurement can be rather subjective, labor-intensive and time consuming. In contrast, photogrammetry can provide an alternative method to obtain relatively simple and fast measurements of JRC based on high resolution 3D models. However, the reliability of such measurements still remains an issue as the results from photogrammetry can be affected by the quality of images. This study seeks to clarify whether photogrammetry can produce accurate measurements of JRC that can be used to assess the stability of slopes. A rock slope with a recent wedge failure in the Gold Coast area, Australia was selected for this purpose, and three different methods such as manual measurements, photogrammetry, and tilt tests were employed to determine the JRC. The obtained results showed some discrepancy in the values of JRC obtained from these three different measurements. In particular, the JRC obtained using the Barton’s comb had slightly higher values compared to those determined through the photogrammetry method while the tilt test results tended to yield overestimated values of JRC. Computer analysis using Universal Distinct Element Code was also performed to study the effect of JRC variation on the slope stability. It was found that an increase in the JRC led to an increase in the safety factor of the slope.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".