Effects of Microbial Induced Desaturation and Precipitation (MIDP) on the cyclic response of sands with varying relative density
Bibliographic record
Abstract
Microbial Induced Desaturation and Precipitation (MIDP) is an emerging bio-mediated ground improvement method which can mitigate liquefaction during seismic loading. In this study, the effect of MIDP on the cyclic response of soils with varying relative densities were evaluated to have a better understanding of the interaction between densification effects and MIDP treatment. A series of undrained cyclic triaxial tests were carried out on untreated fully saturated and MIDP treated Ottawa F60 sand with varying relative densities and treatment levels. The development of pore pressure, axial strain and cyclic stress shear resistance were compared. The results indicated that irrespective of relative density, MIDP can improve the cyclic shear resistance of soil, where performance improves with the increasing treatment level. Both relative density and MIDP treatment enhance the cyclic resistance of soil and the combined effects of them can increase the cyclic resistance further but non-linearly. The liquefaction resistance improvement gained through MIDP is more pronounced in looser soils, suggesting that MIDP can be particularly valuable where densification is limited. Meanwhile, there’s a critical treatment level for the cyclic resistance improvement similar to the effects on the static undrained strength beyond which further treatment leads to no additional improvement.
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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".