Strength and permeability of bentonite-assisted biocemented coarse sand
Bibliographic record
Abstract
To effectively stabilize coarse sand, bentonite was introduced in microbially induced carbonate precipitation (MICP) grouting. Varying concentrations (0, 20, 40, and 80 g/L) of bentonite were added to bacterial suspensions (BSs), which were magnetically stirred to form bacterial-bentonite suspensions (BBSs). Then, coarse sand specimens were treated with BBSs and cementation solutions (CSs) to different cementation levels. The results showed that the addition of bentonite could increase the volume fractions of the precipitates consisting of calcium carbonate (CaCO 3 ) and bentonite. The permeability decreased exponentially as the volume fraction of precipitates increased. As the active precipitates increased when a lower concentration (e.g., 20 g/L) of bentonite was added to the MICP grouting, the unconfined compressive strength (UCS) was substantially improved. However, detrimental effects were observed for specimens treated with a high concentration of bentonite. These results indicate that the effective concentration of natural clay aggregates used in MICP grouting was different for different engineering applications, e.g., seepage control and strength enhancement. The current work provides an encouraging method of improving the MICP technique.
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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 | 0.000 |
| 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) | 0.001 | 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".