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Record W2554604190 · doi:10.1115/ipc2016-64594

Technical and Operational Guidelines When Using Strain Gauges to Monitor Pipelines in Slow Moving Landslides

2016· article· en· W2554604190 on OpenAlexaff
Douglas Dewar, Andy Tong, Edward McClarty, Greg Van Boven

Bibliographic record

VenueVolume 3: Operations, Monitoring and Maintenance; Materials and Joining · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsSpectra Energy (Canada)
Fundersnot available
KeywordsStrain gaugeLandslidePipeline transportExtensometerGeotechnical engineeringReliability (semiconductor)GeologyCalipersPipeline (software)Environmental scienceForensic engineeringEngineeringStructural engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Vibrating wire (VW) strain gauges have been used by the pipeline industry for over 50 years as part of landslide hazard management programs. This paper provides technical and operational guidelines for the use of these strain gauges based on 20 years of experience managing active but slow moving landslides. Guidelines are provided for the use of strain gauges during 1) routine monitoring 2) cut outs and 3) strain relief. Examples of expected strain gauge responses are provided along with technical considerations for interpreting data. Given the relatively small size of the gauges in relation to the length of pipeline within most landslides, techniques are provided to best locate the gauges including the use of 1) visual/on-site geotechnical assessments, 2) geotechnical monitoring technologies and 3) smart pigging technologies (caliper, IMU and axial strain technologies). Limitations, reliability, and alternatives to VW gauges are also discussed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.745

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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 designBench or experimental
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

Citations5
Published2016
Admission routes1
Has abstractyes

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