Effect of freeze–thaw cycles on the macrostructure and failure mechanisms of fiber-reinforced clay using industrial computed tomography
Bibliographic record
Abstract
Freeze–thaw cycling is a critical issue in cold-climate engineering because these cycles impact the mechanical properties of soils due to the translocation of water and ice at temperatures near 0 °C. Reinforcement methods have been developed to decrease these adverse effects, including the use of polypropylene (PP) fibers. However, few macrostructural investigations have been able to demonstrate the underlying physical basis for their effectiveness. This study used computed tomography (CT) images of clay samples reinforced with 2% PP fibers and subjected to unconfined compression and Brazilian tests before and after up to 10 closed-system freeze–thaw cycles (FTCs). Significant effects of the FTCs on soil structure include a reduction in macropores and an increase in mesopores. The addition of PP fibers reduces this change in the number of macropores from 28% to 18% following 10 FTCs. Unreinforced samples also show more localized propagation of shear/tensile cracks during tests than reinforced samples as a result of having a higher failure strength and ductility. The bridging effect of fibers, deviation of the failure path, and formation of microcracks around fibers are clearly illustrated in the CT images. This study provides significant insights relevant to engineering design in cold regions.
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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".