Application of Nonlinear Consolidation Theory to Investigate Slurry/Marsh Soil Consolidation Behavior at Louisiana Coast
Bibliographic record
Abstract
This study delves into the consolidation behavior of Louisiana marsh soil, employing a one-dimensional nonlinear approach and leveraging data from ongoing wetland restoration projects and a series of laboratory experiments.A precise model is developed to accurately predict slurry/marsh consolidation, guided by tactful interpretations of oedometer test results and incorporating specific parameters tailored for large strain consolidation.The research draws on Gibson's nonlinear consolidation theory, implemented in MATLAB, which accounts for essential factors such as the finite strain coefficient of consolidation (𝑔), variable compressibility coefficients (λ), and other soil properties.The study reaffirms the model's robustness through validation against laboratory and field data.The findings, encompassing settlement-time curves, degree of consolidation-time profiles, and void ratio fluctuations along with depth, provide indispensable tools for field engineers in estimating allowable loads, predicting settlement, and designing coastal restoration projects.This contribution enhanced the understanding of self-weight consolidation in geotechnical engineering practices.This research has significant implications for advancing coastal restoration strategies and the sustainable management of wetlands.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".