A coupled bio-chemo-hydro-mechanical model for bio-cementation in porous media
Bibliographic record
Abstract
A key challenge involving microbially induced carbonate precipitation (MICP) is lack of rigorous yet practical theoretical models to predict the intricate biological–chemical–hydraulic–mechanical (BCHM) processes and the resulting bio-cement production. This paper presents a novel BCHM model based on multiphase, multispecies reactive transport approach in the framework of poroelasticity, aimed at achieving reasonable prediction of the produced bio-cement, and the enhanced geomechanical characteristics. The proposed model incorporates four key components: (i) coupling of hydro-mechanical stress–strain alterations with bio-chemical processes; (ii) stress–strain changes induced due to precipitation and growth of bio-cement within the porous matrix; (iii) spatiotemporal variability in hydraulic and stiffness characteristics of the treated medium; and (iv) velocity dependency of the attachment rate of bacteria. The fully coupled BCHM model predicts key unknown parameters during treatment including concentration of bacteria and chemical solutions, precipitated calcium carbonate, hydraulic properties of the solid skeleton, and in situ pore pressures and strains. The model was able to reasonably predict bio-cementation from two different laboratory column experiments. The Kozeny–Carman permeability equation is found to underestimate permeability reductions due to bio-cementation, while the Verma–Pruess relation could be more accurate. A sensitivity analysis revealed bio-cement distribution to be particularly sensitive to the attachment rate of bacteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".