Additive Manufacturing of Soil Using Bio-Cementation
Bibliographic record
Abstract
Microbially Induced Calcite Precipitation (MICP) is an emerging soil treatment technique that is proven to increase strength, decrease erosion, reduce liquefaction risk, enhance slope stability, decrease compressibility, decrease swelling potential, and overall create a more competent soil. The benefits and applications of MICP are broad, and this research seeks to broaden them further by developing a single-phase additive manufacturing application with no treatment time delay. This is done by analyzing layering behaviors of five USCS soil classifications (100 % Ottawa sand, sand clay mixtures, and 100% lean clay) which provides insight into process variables such as the solution volume and layer thicknesses for the additive manufactured specimens. Cuboidal specimens were produced using a layering approach where both bacterial and cementation solutions were applied on the surface of every layer using a volume-controlled spray system. The cuboidal specimens were tested in unconfined compression and the results indicated a notable increase in soil strength for clay soils using this treatment method. This application method evades some complexities commonly faced with fine-grained soils. In addition, the potential of utilizing gel spray solutions for higher levels of control when applying solutions in a defined pattern to create mechanically advantageous shapes were considered. The addition of gel to the treatment solutions reduced bleed and allowed for more control. Both potentials show promise but require more examination. All application specifics are highly variant depending on soil type and would need to be calibrated for site-specific projects.
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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 | 0.001 | 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) | 0.006 | 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 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".