Landslide Mitigation Using Granular Shear Keys: Observations from a Review of 38 Case Studies
Bibliographic record
Abstract
Canadian transportation industries have been using granular shear keys to remediate shallow slope instabilities for decades. Engineers have used limit equilibrium (LE) analyses as the standard method for designing these granular shear keys (Wyllie & Mah, 2004). These LE analyses do not account for the strain required to mobilize the shear resistance in the granular backfill. Finite element methods can model deformation but require calibration and may not always be practical for many applications. Recommendations featuring plots and/or nomograms based on a review of the design and resulting performance of an extensive collection of case histories could supplement current design methods and enhance design effectiveness and reliability. This thesis presents a compilation of 38 case studies for trenched granular shear keys or rockfill column shear keys. The purpose of this compilation was to identify empirical relationships between shear key design and deformation, to enhance the economy and performance of future granular shear keys. Existing granular shear key design guidelines were reviewed and compiled. Case studies featuring granular shear keys were then collected. Details from these case studies were summarized and data was compiled into a database. The data was analyzed and compared with recommendations from the existing guidelines. Additional analyses using deformation data yielded predictions for the magnitude and timespan of post-construction deformation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".