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Record W2610833389

ADVANCEMENT, ASSESSMENT, AND APPLICATION OF NOVEL LANDSLIDE MONITORING TECHNOLOGIES

2017· dissertation· en· W2610833389 on OpenAlexaboutno aff
Nancy Berg

Bibliographic record

VenueQSpace (Queen's University Library) · 2017
Typedissertation
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLandslideComputer scienceEnvironmental scienceEngineeringRemote sensingGeologyGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.705
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.275
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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