Shear Resistance Evolution of Geogrid Reinforced Expansive Soil Under Freeze–Thaw Cycles
Bibliographic record
Abstract
Expansive soils have significant characteristics of expansion by water absorption, contraction by water loss. Under the freeze–thaw (F-T) cycles, the engineering diseases are more significant, and the serious geotechnical engineering incidents are induced extremely easily. The aim is to investigate the mechanical response characteristics of geogrid-reinforced expansive soils (GRES) under F-T cycles. Based on a series of large-scale temperature-controlled triaxial tests, influencing factors were considered, such as the number of F-T cycles, the geogrid layers, and the confining pressure. The results showed that: (1) Friction between the expansive soil and geogrid and the geogrid’s embedded locking effect indirectly provided additional pressure, limited shear deformation. With the increase in reinforced layers, the stress–strain curve changed from a strain-softening to a strain-hardening type. (2) Elastic modulus, cohesion, and friction angle decreased significantly with increasing number of F-T cycles, whereas dynamic equilibrium was reached after six F-T cycles. (3) The three-layer reinforced specimens showed the best performance of F-T resistance, compared to the plain soil, the elastic modulus reduction amount decreases from 35.7% to 18.3%, cohesion from 24.5% to 14.3%, and friction angle from 7.6% to 4.5%. (4) A modified Duncan–Zhang model with the confining pressure, the F-T cycles, and the geogrid layers was proposed; the predicted values agreed with the measured values by more than 90%, which can be used as a prediction formula for the stress–strain characteristics of GRES under freeze–thaw cycling conditions. The research results can provide important theoretical support for the practical engineering design of GRES 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".