Migration and breakthrough of bacteria in heterogeneous soils and stabilization performance of bio-grouting
Bibliographic record
Abstract
Natural soil deposits are typically heterogeneous, leading to changes in solution permeation patterns within heterogeneous soils compared to those in homogeneous soils when employing microbially induced carbonate precipitation (MICP) for soil stabilization. Consequently, estimating the performance of MICP in natural soils solely based on studies in homogeneous soils, as commonly practiced in the past, may not be accurate. In this study, we aimed to investigate the MICP grouting processes in heterogeneous soils with varying levels of heterogeneity under different injection rates. Heterogeneous soils were prepared by connecting two distinct specimens of varying particle sizes through a shared inlet. The permeation patterns and permeabilities were assessed to analyze their correlation. The conversion efficiency and CaCO3 contents were measured to evaluate the efficiency of MICP in stabilizing heterogeneous soils. The results revealed that solution permeation may be primarily influenced by the permeability differences between the two soils, with additional effects from heterogeneity levels and injection rates. Furthermore, using a similar injection strategy as in homogeneous soils in heterogeneous soils could result in preferential flow, leading to nonuniform cementation and wastage of materials. Finally, potential measures were proposed to address these issues, such as monitoring bacterial and reactant concentrations in effluents.
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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) | 0.000 | 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".