Strength, stiffness, and microstructure characteristics of biocemented calcareous sand
Bibliographic record
Abstract
Calcareous sands are known as problematic soils in nature and challenge geotechnical engineers in many practical projects. Microbially induced calcite precipitation (MICP) is an innovative soil improvement technique that uses biomineralisation processes to induce cementation in-situ. The work described in this paper investigates the strength, deformation, and microstructure characteristics of biocemented calcareous sand under different cementation solution to sample volume ratios. A series of laboratory experiments was conducted, including unconfined compressive strength tests, splitting, tensile (i.e., Brazilian) strength tests, and consolidated drained triaxial tests. The results indicate that an exponential function reasonably describes the unconfined compressive strength and splitting tensile strength with increasing cementation solution to sample volume ratios. The tangent modulus at 50% peak strength increases exponentially with an increase in cementation solution to sample volume ratio, whereas it increases linearly with an increase in strength. The strength parameters for this MICP-improved soil, including the peak cohesion and friction angle, are derived to facilitate engineering design. Microstructure analyses are used to illustrate the physical basis for the increase in strength and stiffness with increases in the calcite content, as demonstrated using the cementation solution to sample volume ratio.
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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".