Stacked pile solution for axial load–settlement analysis of driven piles in multi-layered soils
Bibliographic record
Abstract
The load–settlement (Q–s) response of deep foundations is influenced by the soil stiffness. One of the most common methods of installing these foundations is the process of driving, which changes the in situ soil stress and stiffness regime. The stiffness further reduces in a nonlinear manner as the loads and shearing strains increase within the soil. The decay in the stiffness of the soil surrounding an axially loaded pile varies with depth. While a variety of methods is available to predict the nonlinear Q–s response of piles in relatively simpler soil profiles, only select methods can handle the case of multi-layered soils, where the stiffness properties vary between layers. As an alternative, the Randolph analytical pile solution is exploited for (i) developing a new modulus reduction scheme from the back-analysis of load tests on driven piles that also accounts for plasticity of the soil, (ii) devising a methodology for generating modulus reduction curves for individual layers of a multi-layered system, and (iii) formulating a stacked pile model with integration of modulus reduction curves for an improved solution. The back-analysis process accounts for the installation effects on the in situ soil stiffness. A step-wise flowchart and example applications of the methodology are also presented.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".