Reconstructing oedometric compression curves for selecting design parameters
Bibliographic record
Abstract
The mineral structure of soft clays is extremely fragile. Sampling operations and laboratory handling cause unavoidable disturbance. Undisturbed sampling is a theoretical concept because the stress release imposes disturbance. Consolidation tests on disturbed samples provide one-dimensional (1D)-compression curves very different from field response, which leads to inaccurate settlement estimates. Among the approaches for curve reconstruction, Schmertmann’s method is probably the most commonly adopted in engineering practice. However, it presents difficulties in determining preconsolidation stress. The paper primarily addresses Schmertmann’s and Nagaraj et al.’s propositions and discusses their advantages and shortcomings. A new approach for reconstructing the 1D-compression curve is proposed with the main objective being its independence of the interpretation of the experimental results. The validity was examined by comparing the reconstructed curves with those obtained by other methods. Experimental results on high- and low-quality specimens have been analyzed as well. The results revealed that the reconstructed curves for the cases analyzed are almost unique and independent of specimen quality. The proposed method allows both graphical and analytical implementation and the reconstructed curves are unaffected by experimental curve interpretation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".