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Record W3217164088 · doi:10.7939/r3-096d-n503

Assessing The Benefits Of Cost-Effective Monitoring Technology On Unstable Slopes Sensitive To Weather

2021· article· en· W3217164088 on OpenAlexaboutno aff
J. L. Rodriguez

Bibliographic record

VenueUniversity of Alberta Library · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceRemote sensingMeteorologyGeography

Abstract

fetched live from OpenAlex

Across Canada, the presence of ground hazards, such as rock falls, rock slides, or earth slides, next to transportation corridors, is a potential risk to transport system users, the infrastructure, and traffic operations. Monitoring instrumentation systems tracking unstable slopes are a key aspect of risk management for these transportation corridors. These systems improve our understanding of the landslide motion and help establish limits for trigger-action-response-plans (TARP). One crucial aspect of the monitoring system is to quantify ground deformation. Various techniques exist for monitoring landslide deformation. However, Canada's distribution of transportation infrastructure is extensive, and the high cost of applying high precision monitoring systems becomes unfeasible to monitor all active landslides. Thus, cost/effective monitoring instruments are required to provide precise and timely information to assess the failure mechanisms and mitigation actions, especially when sudden weather changes become trigger factors. This thesis presents the results from quantifying and validating the benefits of two low-cost monitoring technology to provide accurate, precise, and reliable data to assess complex failure mechanisms of unstable slopes sensitive to weather changes. A slope failure is considered the movement of a soil mass, and the failure mechanism is the physical process that causes to exceed the soil strength. The monitoring technology for this research included the use of low-cost UAV technology and single-frequency dGNSS receivers. These two monitoring systems have an estimated capital cost of less than $4500.oo CAD per unit, based on the equipment cost acquired for this research and other industrial projects. In addition, the capacity for long-term monitoring to track large surficial deformation provides ease adaptability that permits easy reuse of the system on other areas or sites of interest, reducing further the capital cost of the systems. Three sites were considered for low-cost monitoring: the C018 site and the Chin Coulee landslide in Alberta; and the Ten-mile landslide in British Columbia. The three sites are active, affecting transportation corridors for several years. The analysis of these sites was used to develop a methodology and workflow to apply the monitoring technology. UAV allows engineers to remotely, safely, and quickly perform site inspections of the ground surface. UAV surveys allowed documenting the C018 site changes with photographic records following two rock fall and rockslide events. Photogrammetry methods using the UAV photographs provided detailed and precise point clouds. The point clouds allowed identifying potentially unstable areas, quantify the magnitude of displacement or loss of soil after a movement occurred, and identify multiple failure mechanisms. The analysis demonstrated the benefits of low-cost UAVs as a monitoring tool to accurately assess the unstable slopes. The Ten-mile and the Chin Coulee sites were used for the application of low-cost dGNSS receivers to monitor surficial displacement. The Ten-mile permitted testing the system on a sliding mass disaggregated into smaller blocks. The Chin Coulee permitted testing the lower limits of displacement detection on a translational landslide. A network of dGNSS receivers, called GeocubeTM, installed at each location recorded the ground position in X, Y, Z every 60 seconds between 2017 and 2019. The data allowed calculating displacements and variability in the displacement rates with a ±5 mm precision. Data analysis allowed quantifying the limitations of the Geocube required for early warning systems on landslide deformation. At the Ten-mile the dGNSS system allowed differentiating the moving blocks and movement sequences in a 3D space of the sliding mass. The sliding mass was discretized in five clusters of moving blocks based on a kinematic analysis using the data from the dGNSS system. These clusters were added to a 3D limit equilibrium back analysis to examine the effect of matric suction on the landslide stability. The analyses allowed quantifying the average matric suction needed to achieve a meta-stable condition in the clusters of blocks. This research shows the benefits and limitations of applying two novel low-cost monitoring technologies to assess complex failure mechanisms of ground hazards. A new methodology for the implementation of the Geocube system was developed to monitor landslide movements. The methodology was introduced into the Canadian industry through the application of case studies. The description of the methods used in each case study should provide geotechnical engineers with an adequate understanding to extrapolate the application of these technologies to other ground hazards.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.533

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.201
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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