Investigation of soil setup effects on pile response in clay considering overconsolidation ratio and installation method through physical modeling
Bibliographic record
Abstract
The main objective of this paper is investigation of clayey soil setup effects on axial “resistance” and “stiffness” of small-scale piles with special attention on “overconsolidation ratio” (OCR) and “installation method”. A consolidation chamber is developed and an instrumented model pile is either driven or jacked-in through saturated consolidated Kaolinite clay at different OCRs of 1.2 (normally consolidated, NC) and 4.8 (over consolidated, OC). The pile was static load tested at different time intervals of 1 h through 90 days. Higher pore pressures are induced during pile installation in NC compared to OC clay. The frictional resistance of OC soil is higher than NC at end of drive as well as end of setup in both driven and jacked-in piles. The frictional resistance of jacked-in piles is greater than driven piles at end of drive, but due to higher rate of setup, the long-term frictional resistance of driven piles is shown to be greater, attributed to more disturbance of the soil during installation of driven piles. Attempts are made to differentiate between the contributions of dissipation of excess pore pressure and non-pore pressure components for each consolidation condition and pile installation method. A relation is proposed to quantify the pile stiffness variations over time due to soil setup.
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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) | 0.000 | 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) | 0.001 | 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".