A new state-dependent constitutive model for cyclic thermo-mechanical behaviour of unsaturated vegetated soil
Bibliographic record
Abstract
Climate change has resulted in increasing attention being paid to the effects of temperature and humidity on vegetated soil. However, existing constitutive models rarely investigate the thermo-mechanical behaviour, such as the accumulation of plastic strain under non-isothermal conditions, of unsaturated vegetated soil. To address this issue, this study developed a comprehensive constitutive model by adopting state-dependent dilatancy in conjugation with loading, memory, and bounding surfaces. Moreover, root-induced hardening due to pore occupancy and internal bonding is modelled with dependence on the root volume ratio. The performance of this newly developed constitutive model is then evaluated using previous laboratory element tests. Comparisons between the computed and measured results reveal that the model is capable of capturing variations in soil state at a range of temperatures, suctions, and root volume ratios. The shearing behaviour of vegetated soil, especially its volumetric response, is well predicted by the model, as confirmed by comparing it with triaxial compression test results. Moreover, the model predicts that the accumulated volumetric strain of unsaturated vegetated soil (0.2%) is significantly less than saturated bare soil (1.1%) after four thermal cycles. These computed results imply that vegetation can mitigate the effects of climate change on soil through both transpiration-induced suction and mechanical reinforcement.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".