Modeling swelling–shrinkage behavior of compacted expansive soils during wetting–drying cycles
Bibliographic record
Abstract
This paper presents a straightforward approach for modelling volume change behavior of expansive soils during wetting–drying cycles. The swelling–shrinkage strain of expansive soils induced by cyclic wetting and drying was decomposed with distinctive physical background into a reversible component, which shows a synchronous change with the cyclic change of suction, and an irreversible component, which is generated mainly in the early stage of the wetting–drying process. The mechanisms of the two swelling–shrinkage strain components can be well explained through the double-level structure of expansive soils and its evolution with mechanical and hydraulic loading. The reversible component originates from the reversible deformation behavior of aggregates, and primarily depends on current suction or water content. The irreversible component is associated with the irreversible change of macrostructure, reflecting the difference in soil structures at current state and the equilibrium state. A practical constitutive model was proposed for compacted expansive clays from a global and phenomenological perspective. The model parameters can be calibrated with observed macroscopic deformation behavior without measuring microstructural parameters. The performance of the presented model was validated by simulating cyclic suction-controlled tests as well as an alternately soaked and dried test with irregular amplitudes of suctions.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".