Modeling and Implementation of the Isotache Concept for Long-Term Consolidation Behavior
Bibliographic record
Abstract
A practical use of the isotache concept in creep settlement prediction is newly introduced in this study. The isotache concept introduces a unique relationship between the strain and the consolidation pressure corresponding to the strain rate in association with viscosity. The authors have proposed a method to simply introduce the isotache concept, which uses a compression curve normalized by preconsolidation pressure [ε–log(σ′/σp′)] and a strain rate dependency relationship between the preconsolidation pressure (σp′) and strain rate (ε˙=dε/dt). The strain rate dependency relationship is modeled as an equation based on power law. According to the authors’ method, the slope of the strain rate dependency relationship (logσp′–logε˙), which coincides with Cαe/Cc, can be calculated as a function of strain rate, showing that Cαe/Cc is not constant but decreases with decreasing strain rate. In engineering practice, consolidation settlement is generally estimated based on the compression curve obtained from the 24-h incremental loading oedometer test, which corresponds to a strain rate of about 1×10−7 s−1. Using the compression index Cc at the consolidation pressure and initial void ratio e0, creep settlement can be easily calculated as a function of Cc/(1+e0) and in situ strain rate (several orders of magnitude smaller than 1×10−7 s−1).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".