Experimental validation of a finite strain theory for gassy mud
Bibliographic record
Abstract
In the Netherlands large quantities of polluted mud must be stored in large-scale disposal sites. The storage capacity of these sites and the outflow of contaminated pore water can be predicted by means of one-dimensional finite strain theory. By making various theoretical assumptions, a self-weight consolidation computer program for gassy sludge has been developed. These assumptions are verified by simulating a small-scale column test with biogenic gas production. Good agreement was obtained between the measured and simulated density profiles, excess pore pressures, and settlements. This paper also deals with the validation of simulations with this computer program by means of field measurements from the Slufter disposal site, which is used to store polluted mud from Rotterdam harbour. The field measurements consist of mud level measurements and profiles of water content, gas content, bulk density, and pore-water pressure for two locations in the disposal site. The computer simulations are in reasonable agreement with the field data. The method described here can be used to predict the future disposal capacity on the basis of estimates of the supply of mud and the production of gas.Key words: consolidation, unsaturated, disposal, experimental, validation, soft soil.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".