Examining the Liquefaction Resistance of Lightly Cemented Sands Using Microbially Induced Calcite Precipitation (MICP)
Bibliographic record
Abstract
Microbially induced calcite precipitation (MICP), or bio-cementation, is a bio-mediated, environmentally-conscious soil improvement technology that can improve the engineering properties of granular soils through the precipitation of calcium carbonate (CaCO3) on soil particle surfaces and contacts. While past studies have shown the potential of bio-cementation to improve the resistance of granular soils to earthquake-induced soil liquefaction, the spectrum of behaviors during the transition from the uncemented to lightly cemented conditions has remained poorly characterized. In this study, a series of direct simple shear tests were performed to improve our understanding of the effect of CaCO3 bio-cementation on the liquefaction triggering and post-triggering strain accumulation behavior of loose sands. A poorly-graded Ottawa F-65 sand material was treated to varying degrees of very light bio-cementation and subjected to undrained cyclic shearing events. All specimens were cemented and sheared under a vertical effective stress of 100 kPa and subjected to cyclic stress ratios (CSR) of 0.1 and 0.2. During these tests, shear wave velocity (Vs) measurements were completed to non-destructively monitor cementation progression and degradation during shearing. Results suggest that even very light levels of bio-cementation (ΔVs ≈ 0 to 100 m/s) can significantly increase the number of cycles needed to trigger soil liquefaction (3% single amplitude shear strain), e.g., a 10-fold increase in the number of cycles needed to trigger liquefaction was observed when Vs values were increased by only 25 m/s. Despite this significant improvement in small-strain pre-triggering behavior, little improvement was observed with respect to strain accumulation before and after initial triggering. While additional testing is needed, results from this study can improve our understanding of the behavior of lightly bio-cemented and naturally-cemented soils subjected to earthquake-induced undrained cyclic loading.
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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".