Effect of bio-cementation on geophysical and cone penetration measurements in sands
Bibliographic record
Abstract
Microbially induced calcite precipitation (MICP) is a potentially environmentally conscious ground improvement method that can improve the engineering properties of granular soils through the precipitation of calcite. In this study, an experiment involving two 1.7 m diameter tank specimens was completed to investigate the effect of bio-cementation on cone penetrometer and geophysical measurements in sands. Following nonuniform bio-cementation treatments, specimens achieved calcite contents ranging from 0.5% to 5.3% by mass, shear wave velocity (Vs) values between 131 and 967 m/s, and mid-depth cone penetration resistances (qc) ranging between 3.6 and 32.1 MPa. At calcite contents exceeding 5.0%, qc and Vs improvements were as high as 527% and 686%, respectively. Although cone penetration resistance, sleeve friction, and friction ratio measurements exhibited limited sensitivity to bio-cementation at calcite contents of less than 3.0%, Vs measurements successfully detected bio-cementation at calcite contents near 1.0%. When qc and Vs measurements were compared at similar locations, increases in an empirical parameter (KG) enabled improved detection of bio-cementation at calcite contents near 0.5%. Large increases in normalized tip resistances (Qtn) and small decreases in normalized friction ratios (Fr) with increasing bio-cementation resulted in cemented materials plotting near and within the gravelly sand and sand-like dilative soil behavioral type regions using two soil behavior type (SBT) charts.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".