Investigating challenges of in situ delivery of microbial-induced calcium carbonate precipitation (MICP) in fine-grain sands and silty sand
Bibliographic record
Abstract
Microbial-induced calcium carbonate precipitation (MICP) is a sustainable soil improvement method with the potential for improving the engineering properties of sand and silty soils and therefore their resistance to liquefaction-inducing events. Work presented herein experimentally investigates the changes in hydraulic conductivity of fine sands and silty sands as a result of MICP treatment. In addition, numerical modeling is conducted to assess the changes in allowable injection rate and radius of influence for the delivery of the MICP process at the field scale. The hydraulic conductivity of Nevada sand and silty sand with 15% fines content decreased through MICP application with the trend of reduction being similar for both soils. Numerical modeling results show that with the progress of the MICP process, injection rates can be increased for Nevada sand, but remain unchanged for Nevada sand with 15% silt content (after MICP treatment up to a shear wave velocity about 400 m/s.) The presence of fines by itself leads to generation of higher levels of pore-water pressure during the injection process, which necessitates higher strength improvement to prevent development of excessive plastic strains. Therefore, improvement in shear strength and stiffness relative to the magnitude of the hydraulic conductivity level and its rate of change during the MICP process is a key parameter in determining the radius of treatment.
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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) | 0.000 | 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".