On casting clay specimens of bespoke shear strength and sensitivity for landslide modelling
Bibliographic record
Abstract
Sensitive clay landslides are a geohazard often exhibiting flow-like retrogressive behaviour. Soils with high sensitivity experience significant strain softening − a key characteristic of these types of slope movements. In this paper, an experimental method using high early strength cement mixed with clay soil is investigated to cast repeatable samples of a bespoke shear strength and sensitivity while reducing specimen preparation time. The addition of cement bonds from hydration reactions was observed to encourage the development of a metastable soil structure with sufficiently high moisture content to exhibit both a high peak strength and a low remoulded strength. Various cement and water content mixtures were examined, with either kaolin clay or a naturally sensitive clay from Mud Creek, Ontario and Portland type 1 or type 3 cement. Undrained shear strength testing was measured with the Swedish fall cone and the miniature lab vane. Soil−cement mixtures developed shear strength of up to 60 kPa with sensitivities from 4 to 16 within a 7 d curing time. This paper reports lessons learned from the mixing, curing and testing of the sensitive material, as well as the results from a geotechnical centrifuge experiment examining retrogressive sensitive clay landslides.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".