Liquefaction triggering and post-triggering behavior of biocemented loose sand
Bibliographic record
Abstract
Biocementation is a biomediated ground improvement technique that can improve the engineering behavior of granular soils. The process has received significant attention as an earthquake-induced liquefaction mitigation technique; however, critical gaps have remained in our understanding of how liquefaction behaviors may shift with differences in loading magnitudes and cementation levels. In this study, direct simple shear tests were performed to examine the undrained shearing behaviors of biocemented loose Ottawa F-65 sand prepared to varying cementation levels corresponding to V s increases up to 523 m/s. Significant increases in liquefaction triggering resistances were observed with added cementation across a broad range of loading magnitudes (CSR = 0.1–1.75) and exceeded improvements obtainable through densification alone. Following triggering, modest improvements in post-triggering strain accumulation and reconsolidation behaviors were observed that could be primarily attributed to the densification of specimens from added mineral solids at low cementation levels (Δ V s < 150 m/s). At higher cementation magnitudes, however, post-triggering behavioral enhancements exceeded those that would be expected from densification alone. Outcomes from this study improve our understanding of the liquefaction behaviors of biocemented soils, the metrics by which these behaviors can be effectively characterized, and the mechanisms responsible for behavioral enhancements, ultimately furthering our understanding of how the technology may be employed for liquefaction mitigation.
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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".