Accuracy of determining pre-consolidation pressure from laboratory tests
Bibliographic record
Abstract
Shear strength and compressibility of fine-grained soils is strongly influenced by their stress history and the maximum (pre-consolidation) pressure ([Formula: see text]). Accurate determination of [Formula: see text] is thus critical for settlement and stability analysis involving fine-grained soils. Many graphical techniques are available for estimating [Formula: see text] from the interpretation of soil compression in laboratory consolidation (oedometer) tests. However, the accuracy of these methods has not been extensively proven or compared with each other. A series of 30 laboratory oedometer tests is carried out in this study based on controlled rate of strain and incrementally loaded testing techniques. Several Canadian clay specimens are subject to cycles of one-dimensional compression loading and unloading to produce a known stress history and [Formula: see text]. The imposed [Formula: see text] values are compared with the predictions of 11 methods for determining [Formula: see text]. The accuracies of these methods are subsequently evaluated by comparing their predictions with [Formula: see text] imposed during the consolidation experiments. While these methods mostly overestimate [Formula: see text], it is determined that a bilogarithmic graphical approach based on the slopes of the virgin compression and recompression segments of a soil compression curve provides the most accurate predictions of [Formula: see text]. The potential ranges of errors associated with the application of each method are also presented.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".