Feasibility Study of Water Based / Polymer Modified EICP for Soil Improvement Involving Recycled Glass Aggregate
Bibliographic record
Abstract
Glass is one of the principal waste products generated in the US.The use of these glass cullet in the construction of shoulder section could reduce the quantity of waste glasses that goes to the landfill.Certain type of cementing agent is required to bind these glass particles in shoulder.Enzyme induced carbonate precipitation (EICP) has shown early promise as a viable and sustainable ground improvement method.Water based EICP leads to faster infiltration of cementation solution due to high permeability, thus limiting the amount of available reaction substances to produce CaCO 3 precipitate at desired locations.This problem may be solved to some extent by the use of high viscosity polymer as a carrier of cementation solution in place of water.Laboratory tests performed on the recycled glass cullet showed the possibility of using them in the construction of shoulder section to prevent erosion.Moreover, a series of laboratory experiments performed showed that EICP worked well on the Ottawa sand but did not work well on recycled glass cullet.However, it was successful on the samples containing mixture of glass particles and Ottawa sand.The samples consisting up to 20% of recycled glass in the mixture were brittle and strong.The results of UCS testing showed the compressive strength of the intact sample decreases with increase in amount of recycled glass in the mixture.The pull out test carried out on the glass surface showed the possibility of application of EICP on the surface treated glass particles.SEM, XRD and TGA results on the samples treated with polymer modified EICP verify the presence of CaCO 3 and the strength of the samples were tested at different moisture contents.The treated sand columns were organic-inorganic
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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 | 0.000 | 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 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".