Simplified approaches for predicting the nonlinear load-displacement response of single pile and pile groups in unsaturated soils
Bibliographic record
Abstract
A simplified analytical approach is proposed for predicting the load-displacement behavior of single piles in unsaturated soils considering the contribution from the nonlinear shear strength and soil stiffness influenced by matric suction . This approach includes a Modified Load Transfer Model (MLTM) that can predict the nonlinear relationships between the shear stress and pile-soil relative displacement along the pile shaft, and between the pile base resistance and base settlement. The proposed model is also extended for pile groups to incorporate the interaction effects between individual piles. The analytical approach is validated through a comparative analysis with the measurements from two single pile tests and one pile group test. In addition, a finite element analysis using 3D modeling is carried out to investigate the behavior of pile groups in various unsaturated conditions. This is accomplished with a user-defined subroutine that is written and implemented in ABAQUS to simulate the nonlinear mechanical behavior of unsaturated soils. The predictions derived from the proposed analytical and numerical methods compare well with the measurements of a published experimental study. The proposed methodologies have the potential to be applied in geotechnical engineering practice for the rational design of single piles and pile groups in unsaturated soils.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".