Centrifuge Tests to Investigate the Effect of MICP Treatment Zone on Foundation System Performance
Bibliographic record
Abstract
Ground improvement is often used in engineering practice to mitigate the effects of liquefaction in the built environment. Prior centrifuge studies using bio-cementation (microbially induced calcite precipitation, MICP) have demonstrated increased liquefaction triggering resistance and reduced associated settlements by treating the entire soil model. One additional centrifuge test that treated discrete MICP zones within the soil model also showed improvement, but the complex interactions between the treated zones made it difficult to elucidate how treatment depth impacted system performance. This paper presents 1-m centrifuge experiments performed at the Center for Geotechnical Modeling at the University of California, Davis, to investigate how performance improvement relates to the dimensions and cementation level of a discrete MICP treated zone. Models were constructed with Ottawa F-65 sand at an initially loose state (DR ≤ 50%) with different treatment zone dimensions. In one model, the entire soil body was cemented (18.9 × 39.6 × 8.22 m in prototype scale), while in the second and third models an 8 × 8 m plan area was treated to a depth of 8.22 m (100% depth), and 3.05 m (37% depth). The stiffness levels for the treated zone were selected and tracked using shear wave velocity with a target of ΔVs = 300 m/s relative to an untreated model. In addition, a baseline uncemented model constructed at the same relative density was tested for comparison. All models were subjected to eight shaking events. Shear wave velocity measurements tracked changes in biocementation integrity through the progression of the shaking events. System performance during shaking was monitored using accelerometers, a linear potentiometer, and pore pressure transducers. Cone penetration soundings were used to measure the initial model state and track the model evolution. All models treated with MICP showed improved liquefaction resistance when compared to untreated models, which varied with MICP treatment zone dimensions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.008 |
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 teacher head, 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".