Potassium chloride wells used as quick-clay landslide mitigation: installation procedures, cost–benefit analysis, and recommendations for design
Bibliographic record
Abstract
Retrogressive development of landslides in highly sensitive clays (quick clays) may extend several hundred metres upslope from an initial landslide, and liquified slide-debris may impact buildings or infrastructure in the run-out zone. By installing wells filled with potassium chloride (KCl) in quick clays, the salt migrates into the surrounding clay and increases its remolded shear strength, reducing its sensitivity. The salt-stabilized, nonquick clay volume may act as a barrier preventing backward retrogression, thereby contributing to reducing both the area susceptible for being involved in a quick-clay landslide, and the area of the run-out zone. Installation procedures and design guidelines for salt stabilization are examined herein. Installation procedures generating temporary, very small excess pore-water pressures were tested at National Geo-Test Site Tiller–Flotten. Although the benefit-to-cost ratios related to these procedures are small compared to conventional landslide mitigation measures, reducing the installation costs to less than 30 USD per m well and increasing the center distance between the wells may justify salt-stabilization as a landslide mitigation measure. This paper describes experience from testing safe installation procedures, evaluations of cost–benefit and environmental impact, and proposed design guidelines, introducing KCl as an alternative to conventional landslide mitigation-measures in slopes with highly sensitive quick-clay deposits.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".