The infiltration characteristics of expansive soil considering fracture under wet-dry cycle conditions
Bibliographic record
Abstract
Expansive soils exhibit a relatively low permeability coefficient when structurally intact, allowing for their treatment as a homogeneous medium in calculations. However, the susceptibility of the slope's shallow area to numerous primary and secondary cracks under the influence of wetting and drying cycles challenges this approach. Failing to account for the impact of these surface cracks on the soil's permeability can result in a significant discrepancy between calculated and actual conditions. This study initially validated a predictive model for the soil-water characteristic curve that incorporates the effects of wetting and drying cycles. Subsequently, leveraging the fracture volume ratio parameter (pv) and the bimodal distribution characteristics of the dual-pore structure, we proposed a permeability coefficient model for expansive soils that considers fracture effects. This model was integrated with the validated soil-water characteristic curve model to facilitate the analysis of expansive soil's infiltration characteristics under cyclic wetting and drying conditions. The findings indicate that the predictive model accurately captures the hysteresis effect of expansive soil's soil-water characteristics. Moreover, the permeability coefficient model, which accounts for fractures, effectively reflects the infiltration properties of cracked expansive soil and enables the prediction and calculation of its permeability under multiple cycles of wetting and drying. This study introduces a predictive model for the soil-water characteristic curve, leveraging the hysteresis properties of expansive soil. Additionally, it presents a model for calculating the permeability coefficient of expansive soil, utilizing a dual-peak characteristic function. The development of these models establishes a theoretical basis for the computation and analysis of the soil's permeability attributes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".