Effect of Light Biocementation on the Liquefaction Triggering and Post-Triggering Behavior of Loose Sands
Bibliographic record
Abstract
Microbially induced calcite precipitation (MICP) is an environmentally conscious ground-improvement method that can enhance the engineering properties of granular soils through the precipitation of calcium carbonate (CaCO3) on soil particle surfaces and contacts. Although numerous studies have shown the ability of biocementation to improve the liquefaction resistance of loose sands, the effects of light cementation levels on undrained cyclic behaviors have remained relatively unexplored. A series of undrained monotonic and cyclic direct simple shear tests were performed to examine the effect of light biocementation (ΔVs<100 m/s and CaCO3 contents <0.9%) on the liquefaction triggering and post-triggering behavior of loose Ottawa F-65 sand subjected to varying loading magnitudes [cyclic stress ratio (CSR)=0.1 to 0.3]. Results suggest that the presence of light biocementation can significantly improve the liquefaction triggering resistance of loose sands, with log-linear increases in the number of cycles required to trigger liquefaction, which consistently correlated with cementation-induced Vs increases. Despite these remarkable pretriggering improvements, almost no improvements were observed in post-triggering strain accumulation and postcyclic reconsolidation behaviors, with Vs measurements indicating that small-strain improvements were largely erased following shearing events.
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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".