Development and use of axial load test databases for analysis and design of soft rock sockets
Bibliographic record
Abstract
This paper presents the state of knowledge of analysis and design of rock sockets—main features of axial loading behavior and influential factors, methods for predicting the mobilized strength (shaft shearing fs and end bearing qb) and load–displacement ( Q– w) curves. A total number of 363 field load tests on full-scale rock sockets are compiled into a database—DUT/PileROC/363 and analyzed in a consistent manner. This database is used to evaluate the variability of predictions of mobilized resistances ( fs and qb), loads Qa at settlements w = 5–20 mm and 0.5%–2% of socket diameter, and Q– w curves. For fs, qb, and Qa, the results are characterized as the mean and coefficient of variation (COV) of the ratio of measured over predicted values. It is observed that (1) both shaft shearing and end bearing can be mobilized at small displacements when good construction and inspection procedures are followed, (2) the majority of empirical models for fs and qb are too simplistic (solely accounting for the unconfined compressive strength of rock) and the resulting predictions are highly dispersive (COV > 0.6), (3) the prediction quality does not necessarily improve with an increased level of model sophistication, (4) rock mass modulus Em is crucial for predicting Qa and Q– w curves, (5) predictions of Qa by one nonlinear and two side-slip design methods are of medium dispersion (COV = 0.3–0.6), and (6) only the nonlinear design method produces Q– w curves that resemble the shape of measured Q– w curves in load tests.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".