Bio-grouting to enhance axial pull-out response of pervious concrete ground improvement piles
Bibliographic record
Abstract
Bio-grouting is an environmentallly friendly, sustainable, and low-cost ground improvement technique, which mainly utilizes microbial-induced carbonate precipitation. Previous large-scale applications of MICP have encountered practical difficulties including bio-clogging, which resulted in a limited zone of cemented soil around injection points. The research presented in this paper focuses on evaluating the feasibility of cementing a limited soil zone surrounding permeable piles using MICP bio-grouting to improve the mechanical response of permeable piles under axial pull-out loading. Two instrumented pervious concrete piles (test units), one with and one without MICP bio-grouting, were subjected to pull-out loading at the Soil-Structure Interaction Facility at Lehigh University. The pervious concrete pile served as an injection point during the MICP bio-grouting. The mechanical responses of the test units and surrounding soil were analyzed, along with shear wave (S-wave) velocities, moisture, and CaCO3 contents of the surrounding soil. The results presented in this paper demonstrate that the limited MICP-improved zone, extending a radial distance of approximately 102 mm around pervious concrete piles, improved the load–displacement response, load transfer, and pile capacity under pull-out loading. The ratios between ultimate loads of the test units with and without MICP bio-grouting were 4.2. The average shaft resistance along the pile with MICP bio-grouting was up to 2.8 times higher than that of the pile without bio-grouting.
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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) | 0.001 | 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".