A coupled nonlinear long-term consolidation and solute transport model for PVD-enhanced flushing remediation analysis of multilayered contaminated soils
Bibliographic record
Abstract
The remediation of contaminated fine-grained soils through flushing, enhanced by prefabricated vertical drains (PVDs), involves a complex coupled process of axisymmetric consolidation and solute transport. However, existing models often oversimplify these interactions and fail to accurately capture real vacuum pressure boundary conditions. This study presents a coupled nonlinear model that integrates PVD-assisted consolidation and solute transport for multilayered contaminated soils. The governing equations are solved using the finite difference method, and the numerical solution is validated against first analytical solutions for simplified axisymmetric models, then soil-flushing experiments and consolidation-induced solute transport tests. Furthermore, the developed model is applied to assess the effects of key engineering design parameters, including vacuum pressure in PVD, PVD spacing, and PVD penetration length, on clean-up efficiency. Parametric analyses indicate that increasing vacuum pressure and reducing the PVD spacing can improve the clean-up efficiency along the radial direction. However, the treatment depth cannot be significantly enhanced by merely increasing vacuum pressure or reducing the PVD spacings. These findings provide insights into optimizing the design of PVD-enhanced soil flushing systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".