An Examination of the Effect of Chemically Induced Damage on the Monotonic and Cyclic Shearing Behavior of Biocemented Sands
Bibliographic record
Abstract
ABSTRACT Microbially induced calcite precipitation is a biomediated soil improvement method that can improve the engineering properties of granular soils. Although improvements in soil engineering behaviors afforded by biocementation have been extensively characterized, there remains limited understanding of the anticipated long-term engineering behavior of biocemented soils following progressive chemical damage that may be experienced following initial applications. In this study, 10 direct simple shear tests were performed to investigate the effect of chemically induced damage on the drained monotonic and undrained cyclic shearing behaviors of biocemented loose Ottawa F-65 sand. All specimens were either uncemented, biocemented to different cementation levels corresponding to shear wave velocity increases (ΔVs) between 150 and 500 m/s, or biocemented to a ΔVs near 250 or 500 m/s and then subjected to degradation injections, which induced chemical damage and achieved ΔVs reductions of either 100 or 200 m/s. For all specimens, Vs and soil calcium carbonate content measurements were performed to assess improvement magnitudes, cementation uniformity, and evaluate magnitudes of chemically induced damage. As expected, increases in biocementation levels as captured by Vs increases were shown to progressively improve drained monotonic and undrained cyclic shearing behaviors. Following chemically induced damage, however, behavioral improvements were largely retained and were found to be consistent with the nondegraded biocemented specimens on the basis of similar Vs values. The performed tests provide the first examination of the expected long-term engineering behaviors of biocemented sands and yield new understandings regarding the anticipated impacts of chemical damage on behaviors relevant to subsurface liquefaction mitigation applications and other geotechnical use cases.
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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".