Liquefaction Strength of Ottawa Sand: CDSS Experiments and ANN Modeling
Bibliographic record
Abstract
Characterization of the liquefaction strength of sandy soils is essential in modeling geotechnical engineering problems involving liquefiable soils. This paper investigates the liquefaction strength of Ottawa F65 sand through an extensive series of undrained, stress-controlled, cyclic direct simple shear (CDSS) tests performed at different densities, overburden pressures, and static shear stresses prior to cyclic shearing. The relatively large number of CDSS tests is used to develop an artificial neural network (ANN) model in order to predict Ottawa F65 liquefaction strength for densities and loading conditions that are not available in the experimental results. The predictive capability of the ANN model is assessed using blind predictions of the cyclic strength in new CDSS tests for a relative density and vertical effective stress not available in the training data set. Afterwards, CDSS tests under similar conditions were carried out. The comparisons of the predictions with the experimental results suggest that the ANN model provides reasonably good and conservative predictions of the soil liquefaction strength and shows sensitivity to changes in vertical effective stress, soil density, and cyclic stress ratio.
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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 | 0.001 | 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".