A simplified nonlinear settlement prediction model for foundations on sand
Bibliographic record
Abstract
This paper presents a simple method for predicting the settlement of spread foundations on sand operating under typical working loads. The method accounts for the well known effects on soil stiffness of strain, stress level, and density dependence, but adopts the simplifying assumption that the stress distribution beneath a loaded foundation can be obtained from Boussinesq's equations for an elastic half space. Despite this simplification, the method is shown to predict foundation responses that closely match those computed using more sophisticated finite element (FE) analyses and those measured in laboratory footing tests, where the stiffness characteristics in triaxial compression were well defined. The method is also seen to predict general variations of foundation settlement with bearing pressure, foundation width, and degree of preloading that are entirely consistent with empirically observed trends. It is concluded that satisfactory settlement predictions for shallow foundations on cohesionless soil may be obtained using Boussinesq's equations if the soil's vertical stiffness characteristics, as inferred from triaxial compression data, can be specified with some degree of precision.Key words: stiffness, settlement, cohesionless soil.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".