Cementing Mechanism of MICP-Treated Mortar and Performance Improvement by Innovative Molds
Bibliographic record
Abstract
Microbial induced carbonate precipitation (MICP) has the potential to have less hazardous impacts on the environment compared with traditional reinforcement technologies. In this paper, the mechanical property and cementing mechanism of MICP-treated mortar (MTM) are studied, and the double-layer rigid soaking mold is invented to prepare high-strength MTM samples. The effects of the cementation solution concentration (CSC), the concentration ratio of urea to calcium chloride (CRUC), aggregate particle size, and soaking time on the mechanical properties of MTM are researched. The results show that the strength of the MTM sample increases first and then decreases with the increase of CSC. The mean UCS of MTM samples reaches the peak of 8.19 MPa when the CSC is 1.5 M. The strength performance of MTM samples is relatively better when the CRUC is 1. For MTM samples with graded particle size, the sample with the particle size of 0.4–0.8 mm has the highest strength of 5.03 MPa. For MTM samples with full particle size, the mean UCS increases from 1.18 to 12.88 MPa with the increase of the maximum particle size from 0.2 to 2 mm. The MTM sample with full particle size has a higher strength when the maximum particle size is larger than 0.8 mm. The strength of MTM samples increases within 9 days over the soaking time and then tends to be stable at the later stage. The calcium carbonate mineral in the MTM sample is mainly calcite and a small amount of vaterite, and the strength of MTM is positively correlated with its CaCO3 content. The CaCO3 content of the sample shows a high surrounding and low middle distribution.
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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".