One-dimensional consolidation behavior of cement-treated organic soil
Bibliographic record
Abstract
This paper addresses the effects of cement treatment on the one-dimensional (1D) consolidation behavior of a highly organic soil (LOI ∼ 40%–60%, where LOI is the loss on ignition), based on 1D constant rate of strain and incremental loading tests. The effects of Portland cement addition are evaluated for dosages ranging from 8% to 100% by dry mass of soil, corresponding to values of the cement factor of 24 and 296 kg of cement per cubic metre of untreated soil, within the range used in deep mixing practice. Additional parameters investigated are the impact of curing surcharge and duration. The most evident effect of the treatment is the development of a cementation-induced preconsolidation stress: the greater the cement dosage, the greater the preconsolidation stress and the greater the vertical effective stress that can be sustained at any void ratio. The results also provide a consistent picture of the effects of cement treatment on stiffness, hydraulic conductivity, coefficient of consolidation, and creep. Comparison to data obtained for the untreated soil demonstrates the “stable” nature of the structure generated as a result of treatment. The consolidation results are complemented by pH measurements, extraction tests, elemental analyses, and Fourier transform infra-red (FTIR) spectroscopy analyses, which provide insight into the interaction between soil organic matter and cement.
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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".