Analysis of One-Dimensional Consolidation for Double-Layered Soil with Non-Darcian Flow Based on Continuous Drainage Boundary
Bibliographic record
Abstract
The boundary drainage performance controls the rate of pore water discharge in the soil and plays an important role in the prediction of soil consolidation and settlement. Based on a continuous drainage boundary that can reflect the change of boundary drainage performance with time, a one-dimensional consolidation model of double-layered soil considering non-Darcian flow is established. The finite-difference method and semianalytical method are used to solve the consolidation equation, and the reliability of the two methods is verified by comparing with existing solutions. Based on the proposed solution, the consolidation behaviors of the double-layered soil are explored in depth through a systematic parametric study. The results show that, if the time effect of drainage boundary and the influence of non-Darcian flow are ignored, the estimated consolidation rate is relatively fast in the whole consolidation stage. The non-Darcian flow has a greater influence on soil consolidation under the continuous drainage boundary condition compared with that under the traditional drainage boundary condition. The consolidation rate of the foundation can be improved by appropriately increasing the permeability of the underlying soil layer or decreasing the compressibility of the underlying soil layer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".