Driven cast-in-situ pile capacity: insights from dynamic and static load testing
Bibliographic record
Abstract
Driven cast-in-situ (DCIS) piles are classified as large displacement piles. However, the use of an oversized driving shoe introduces additional complexities influencing shaft resistance mobilisation, over and above those applicable to preformed displacement piles. Therefore, several design codes restrict the magnitude of shaft resistance in DCIS pile design. In this paper, a series of dynamic load tests was performed on the temporary steel driving tubes during DCIS pile installation at three UK sites. The instrumented piles were subsequently subjected to maintained compression load tests to failure. The mobilised shear stresses inferred from the dynamic tests during driving were two to five times smaller than those on the as-constructed piles during maintained load testing. This was attributed to soil loosening along the tube shaft arising from the oversized base shoe. These radial stress reductions appear to be reversible by the freshly cast concrete fluid pressures, which provide lower-bound estimates of radial total stress inferred from the measured shear stresses during static loading. This recovery in shaft resistance is not recognised in some European design practices, resulting in conservative design lengths. Whilst the shaft resistance of DCIS piles was underpredicted by the dynamic load tests, reasonable estimates of base resistance were obtained.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".