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 f s and end bearing q b ) 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 ( f s and q b ), loads Q a at settlements w = 5–20 mm and 0.5%–2% of socket diameter, and Q– w curves. For f s , q b , and Q a , 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 f s and q b 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 E m is crucial for predicting Q a and Q– w curves, (5) predictions of Q a 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 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 | 0.001 | 0.001 |
| 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".