Numerical Evaluation of Drag Force on Integral Abutment Piles
Bibliographic record
Abstract
Piles transmit structural loads through skin friction, end-bearing, or both to deeper and stronger soil layers. Surcharge loads, site grading, or dewatering activities induce downward movement in the soil that is adjacent to piles installed in a compressible soil layer. This movement causes negative skin friction stresses that act downward at the pile–soil interface, which causes an additional force denoted the drag force (Qn) that is applied to the pile, which results in a larger axial load in the pile shaft. The end-bearing force and positive skin friction that develops in the pile part within stable (incompressible) soil resist the applied loads [e.g., dead load (Qd) and Qn]. This paper aimed to evaluate Qn that was mobilized on driven H-piles that were installed in soft clay with three-dimensional (3D) nonlinear finite-element analysis. Two numerical models were validated against the field data from two instrumented H-piles, which were part of a three-span bridge (E-21) on Highway 418, Ontario, Canada. The calculated settlements and Qn agreed well with measured field data. The validated numerical models were employed to conduct a parametric analysis to investigate the location of the neutral plane (NP) at which the skin friction changed from negative to positive. In addition, the performance of the piles that were installed in small and large groups was investigated, which considered Qn and group effects. It was found that the group effect was negligible for piles that were installed in one row but were significant for piles that were installed in large groups. Finally, a group factor was proposed to calculate the drag force for piles in a group (Q(n)pile in group).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".