Biogrouting coarse materials using soil-lift treatment strategy
Bibliographic record
Abstract
This paper investigates the feasibility of using a soil-lift biogrouting treatment strategy to improve the mechanical properties of coarse sand with the view of applying it to stone columns – sand piles and rammed earth columns type of applications. A two-phase percolation approach was adopted in this study that included percolating a bacterial suspension Sporosarcina pasteurii in the first phase and a cementation solution in the second phase. This process was repeated every two treatments. The study reveals that an increase in the number of soil lifts negatively influenced the mechanical properties of the biocemented coarse sand. However, the minimum strength and stiffness achieved (2.8 MPa) in this study was sufficient to mitigate slumping of a soil column that may occur during installation or excessive radial expansion. Furthermore, it is shown that a single lift treatment can lead to a very high increase in strength and stiffness (up to 8.9 MPa and 2.3 GPa, respectively). However, calcite distributions within biocemented soil columns piles were quite heterogeneous with increasing number of soil-lift treatments. Soil-lift treatment can be seen as a practical strategy that can be used to inject treatment liquids in deeper depths, such as in soil columns piles.
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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 | 0.001 |
| 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".