Experimental Study on the Reliability of Scaling Down Techniques Used in Direct Shear Tests to Determine the Shear Strength of Rockfill and Waste Rocks
Bibliographic record
Abstract
The determination of shear strength parameters for coarse granular materials such as rockfill and waste rocks is challenging due to their oversized particles and the minimum required ratio of 10 between the specimen width (W) and the maximum particle size (dmax) of tested samples for direct shear tests. To overcome this problem, a common practice is to prepare test samples by excluding the oversized particles. This method is called the scalping scaling down technique. Making further modifications on scalped samples to achieve a specific particle size distribution curve (PSDC) leads to other scaling down techniques. Until now, the parallel scaling down technique has been the most popular and most commonly applied, generally because it produces a PSDC parallel and similar to that of field material. Recently, a critical literature review performed by the authors revealed that the methodology used by previous researchers to validate or invalidate the scaling down techniques in estimating the shear strength of field materials is inappropriate. The validity of scaling down techniques remains unknown. In addition, the minimum required W/dmax ratio of 10, stipulated in ASTM D3080/D3080M-11 for direct shear tests, is not large enough to eliminate the specimen size effect (SSE). The authors’ recent experimental study showed that a minimum W/dmax ratio of 60 is necessary to avoid any SSE in direct shear tests. In this study, a series of direct shear tests were performed on samples with different dmax values, prepared by applying scalping and parallel scaling down techniques. All tested specimens had a W/dmax ratio equal to or larger than 60. The test results of the scaled down samples with dmax values smaller than those of field samples were used to establish a predictive equation between the effective internal friction angle (hereafter named “friction angle”) and dmax, which was then used to predict the friction angles of the field samples. Comparisons between the measured and predicted friction angles of field samples demonstrated that the equations based on scalping scaling down technique correctly predicted the friction angles of field samples, whereas the equations based on parallel scaling down technique failed to correctly predict the friction angles of field samples. The scalping down technique has been validated, whereas the parallel scaling down technique has been invalidated by the experimental results presented in this study.
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 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".