A numerical technique for modeling the behavior of single piles in unsaturated soils
Bibliographic record
Abstract
Pile foundations are widely used in both saturated and unsaturated soils. In certain scenarios, these foundations are subjected to combined vertical and lateral loads. Conventionally, saturated soil mechanics principles are routinely used for the design of pile foundations in unsaturated soils. Such approaches contribute to unreliable estimates of the behavior of piles due to ignoring the influence of matric suction. In this paper, a comprehensive numerical technique is proposed for simulating the behavior of single piles subjected to combined vertical and lateral loads in unsaturated soils by taking account of the nonlinear behavior of shear strength and the elastic modulus of unsaturated soils. This is achieved through a subroutine that was developed for use in the ABAQUS software. The proposed numerical method provided reliable prediction of the vertical load-displacement behavior of a published model pile tested in saturated and unsaturated sands. In addition, 3D finite element analysis was extended to simulate the influence of variations in ground water table (GWT) on the vertical bearing capacity and the influence of vertical loads on lateral response of piles. The proposed numerical technique is a promising tool for implementing the state-of-the-art understanding of the mechanics of unsaturated soils into conventional engineering practice.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".