Modelling the applied vertical stress and settlement relationship of shallow foundations in saturated and unsaturated sands
Bibliographic record
Abstract
The bearing capacity and settlement of foundations are determined experimentally or modelled numerically based on conventional soil mechanics for saturated soils. In both methods, bearing capacity and settlement are estimated based on the applied vertical stress versus surface settlement relationship. These methods are also conventionally used for soils that are in an unsaturated condition, ignoring the contribution of matric suction. In this study, a methodology is proposed to estimate the bearing capacity and settlement of shallow foundations in unsaturated sands by predicting the applied vertical stress versus surface settlement relationship. The proposed method requires soil parameters obtained under only saturated conditions (i.e., effective cohesion, effective internal friction angle, and modulus of subgrade reaction from model footing test) along with the soil-water characteristic curve (SWCC). In addition, finite element analyses are undertaken to simulate the applied vertical stress versus surface settlement relationship for unsaturated sands. The proposed method and finite element analyses are performed using an elastic – perfectly plastic model. The predicted bearing capacities and settlements from the proposed method and finite element analyses are compared with published model footing test results. There is good agreement between measured and predicted results.
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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".