ADVANCEMENT, ASSESSMENT, AND APPLICATION OF NOVEL LANDSLIDE MONITORING TECHNOLOGIES
Bibliographic record
Abstract
Slopes are often monitored by measuring deformation rates, or the factor of safety is estimated through the use of limit equilibrium stability models to evaluate the risk of failure. In this thesis, three novel landslide monitoring technologies are advanced, assessed, and applied using three strategically chosen field sites. Firstly, it was investigated if acoustic emissions (AE) could be used to measure the displacement of extremely slow-moving landslides. By installing a shallow and a deep AE sensor as well as a ShapeAccelArray (SAA) in a slope located in Peace River, Alberta, it was found that data from a shallow AE sensor allows noise to be filtered from the deep AE sensor data, and that smaller displacement rates than previously expected can be measured using an AE sensor. The second monitoring advance explored a method of measuring 3D slope displacement using digital image correlation (DIC) performed on hillshade images (image of shaded point cloud data). Through the use of synthetic movement tests and experimental data, it was found that 3D displacement can be measured using hillshade images at two different view angles and that small deformations leading to failure can be measured allowing the time to failure to be calculated. Thirdly, point cloud data produced from historical air photos was used to investigate whether historical slope profiles could be produced to serve as a quantitative baseline of historical landslide activity. This hypothesis was tested to investigate the impact of land-use change on retrogressive landslides occurring along a waterway. Natural revegetation of the area around the creek was observed to result in a significant decrease in the volume of landslide debris generated by geomorphic processes. Finally, back analyses were conducted to estimate the mobilised shear strength at failure through the use of monitoring data from two 3.5 metre high earth dams brought to failure. This unique dataset provided an assessment of the repeatability of the back analyses and showed that only a small component of apparent cohesion arises from a combination of dilation, unsaturated soil behaviour, or root reinforcement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".