Dynamic and Static Characterization of Bio-Cemented Soils
Bibliographic record
Abstract
ABSTRACT: Microbially induced calcite precipitation (MICP) is a ground improvement technique where the metabolic activities of bacteria are utilized to produce calcium carbonate precipitants at the grain contacts or within the pore spaces, enhancing bonding between the soil particles thus generating artificially cemented rocks with improved stiffness/strength properties. A key challenge in practical implementation of this technology is determining the percentage of the produced bio-cement and characterization of the resulting weakly consolidated rocks. Non-destructive techniques (NDT) are ideal for monitoring the percentage of the produced bio-cement, which is of a non-uniform and heterogenic nature, as well as the characteristics of the bio-induced cemented rock. In this study, we have conducted a series of controlled laboratory column tests where various bio-cemented samples were produced. NDT experiments including S-wave velocity measurements were taken along samples to determine bio-cementation and characterize the enhanced strength properties. Advanced signal processing techniques were conducted to interpret the findings. Results were compared against unconfined compressive tests (UCS). Findings provide a novel insight into utilization of NDT and interpretation of the results for charactering weakly consolidated rocks. 1. INTRODUCTION Microbiological-induced calcite precipitation (MICP) is a ground improvement technique that utilizes ureolytic microorganisms capable of hydrolyzing urea to generate calcium carbonate minerals, referred to as bio-cement (Stocks-Fisher et al. 1999). The resulting enhanced engineering properties in soils treated through MICP are governed by the distribution of bio-cement at the pore-scale, which is heterogeneous and complex in nature. When the produced bio-cement is precipitated at the soil particle contacts, it can enhance bonding between soil particles thus improve shear stiffness and shear strength, compressibility and dilatancy tendencies, and liquefaction resistance (e.g., DeJong et al., 2006; Martinez and DeJong, 2009; Montoya and DeJong 2015). The hydraulic characteristics of bio-treated soils however are found to be mainly impacted by the percentage of the bio-cement produced in the pores (pore filling).
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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".