Experimental study of mechanical behaviours of lime-treated Quebec silty clay soils under freeze–thaw cycles
Bibliographic record
Abstract
In cold regions, environmental factors significantly affect infrastructure such as roads, highways, and pipelines. Although lime and limestone products have been used to stabilise soil and enhance its engineering properties, their application in subarctic regions has been limitedly investigated. This study examines the effect of freezing and thawing on the mechanical responses of lime-treated soils retrieved from northern Canada. Samples were stabilised with lime and subjected to a curing period of up to 28 days to ensure consistent humidity and temperature. The cured samples were evaluated for unconfined compressive strength (UCS) and indirect tensile strength using a custom-designed measurement facility considering the number of freeze–thaw (F-T) cycles, loading rates, and curing duration. The double-punch test is shown to be effective in capturing samples’ brittle tensile behaviour. Our results demonstrate that both the UCS and tensile strength are significantly improved by adding lime to the soil. Both strengths generally degrade during the first ten F-T cycles. However, lime-treated soils begin to regain strength after ten F-T cycles. In addition, our test results show that the lime-treated soil is sensitive to exposure to ambient moisture. Nevertheless, the lime-treated soil still retains a much higher UCS compared with the natural soil.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".