Tests and Analytical Model to Predict Geotextile Tube Performance in the Field: A Case Study
Bibliographic record
Abstract
Geotextile tubes are used in dewatering applications over many decades for a variety of slurries, sediments, and wastes. With the increased use of geotextile tubes dewatering in recent years, the desire to maximize both the dewatering rate and retention lead to the use of chemical coagulants and flocculants, which has become a standard practice in geotextile dewatering projects. A variety of small-scale, medium-scale, pilot-scale test methods, and models are used to predict geotextile tube dewatering performance in field. In addition, analytical models have been developed using pilot-scale test and pressurized 2-dimensional dewatering test (P2DT) to predict the dewatering behavior in field and in the lab. These analytical models can be used to predict the dewatering behavior under alternative conditions, including the changes in pumping rates, solids concentration of the slurry, number of dewatering cycles, dewatering duration, final solids concentration of filter cake, and in cumulative volume of slurry. Analyzing the alternative dewatering scenarios using analytical models prior to full-scale implementation, without conducting many dewatering performance tests, is a great benefit in terms of time and money. This study focuses on a geotextile dewatering project of a glue industry settling pond, and the material had different geotechnical properties from the traditional dredged sediment. Multiple lab and field tests were conducted in this study and analytical model was used to evaluate the dewatering performance of geotextile demonstration tests (GDT) in the field. It was found that GDT results were close to the P2DT results and the analytical model successfully predicted GDT results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".