Biopolymer-modified bentonite slurry with gellan gum for high salt resistance in marine geotechnical engineering
Bibliographic record
Abstract
The performance of bentonite slurry in submarine tunnel construction is significantly deteriorated by high-salinity seawater intrusion, increasing the risk of excavation face instability. This study investigates the incorporation of the biopolymer gellan gum (GG) into the bentonite slurry, as a high salt resistant and eco-friendly additive. The mechanisms underlying seawater-induced deterioration and GG-imparted salt resistance were analyzed. Results demonstrate that seawater increases the bleeding rate and American Petroleum Institute (API) fluid loss while reducing the apparent viscosity and yield stress of bentonite slurry, thereby compromising its shear-thinning properties. The addition of GG mitigates these negative effects and improves the colloidal stability, rheological properties, and filter cake quality of the slurry. With 1.2% GG, the 24 h bleeding rate of slurry was reduced from 86.4% to 0%, and a low-permeability filter cake (5.6 × 10−9 m/s) was rapidly formed, as confirmed by sand infiltration tests. Scanning electron microscopy analysis revealed that GG re-disperses seawater-induced bentonite platelet aggregates, while Fourier transform infrared spectroscopy results highlight the synergistic effects of cation consumption and gel filling by GG. This study highlights the potential of GG as a sustainable additive for bentonite slurry in marine geotechnical engineering applications.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".