Cementation of sand soil by microbially induced calcite precipitation at various degrees of saturation
Bibliographic record
Abstract
A newly emerging microbiological soil stabilization method, known as microbially induced calcite precipitation (MICP), has been tested for geotechnical engineering applications. MICP is a promising technique that utilizes the metabolic pathways of bacteria to form calcite precipitation throughout the soil matrix, leading to an increase in soil strength and stiffness. This paper investigates the geotechnical properties of sand bio-cemented under different degrees of saturation. A series of laboratory experiments was conducted, including sieve analysis, permeability, unconfined compressive strength, consolidated undrained triaxial, and durability tests. The results indicate that higher soil strength can be obtained at similar CaCO 3 content when the treatment is performed under a low degree of saturation. The experimental results are further explained with a mathematical model, which shows that the crystallization efficiency, i.e., actual volume of crystals forming at the contact point where they contribute the most to strength, can be calculated from the degree of saturation and grain size. Fine sand samples exhibited higher cohesion, but lower friction angle than coarse sand samples with similar CaCO 3 content. The results also confirm the potential of MICP as a viable alternative technique for soil improvement in many geotechnical engineering applications, including liquefiable sand deposits, slope stabilization, and subgrade reinforcement. The freeze–thaw and acid rain resistance of MICP-treated sand has also been tested.
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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".