The effect of soil fabric on shrinkage behaviour and microstructure evolution of soft soils upon drying
Bibliographic record
Abstract
Soft soils often contain important fractions of fines and organic matter which are characterized by high shrinkage potential. Investigating the shrinkage behaviour of soft soils is of primary importance to assess the durability of infrastructures exposed to climatic stresses. This paper investigates the influence of soil fabric on the shrinkage properties and the change in the microporosity structures over drying. Soil shrinkage tests were performed on Shenzhen soft clay (SZ soft clay) and fibrous peat. The effect of the initial fabric was investigated by comparing the samples at natural and reconstituted states, and the reconstituted peat samples with different fibre contents. To assist in the interpretation of the results, microstructural changes in the soil fabric were investigated by mercury intrusion porosimetry, scanning electron microscopy, and micro-CT scan. As the water gradually drains from the multilevel pores, different drying stages were identified. Pore refinements occurred during the drying process, accompanied by a progressive reduction in the peak pore entrance. Contrary to SZ soft clay, intensive shrinkage occurred in the last stage of fibrous peats, due to the collapse of interpores and cracks created during drying. The results showed that the magnitude of the intensive shrinkage increased with the fibre content. It suggests that fibres act as an unstable element in peat upon drying.
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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 | 0.000 | 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".