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Record W4399332037 · doi:10.1680/jphmg.23.00060

Pressure and thermal effects on Rayleigh fibre-optic strain measurement for soil–structure interaction

2024· article· en· W4399332037 on OpenAlexaff
Geoff Eichhorn, Stuart K. Haigh

Bibliographic record

VenueInternational Journal of Physical Modelling in Geotechnics · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsQueen's University
Fundersnot available
KeywordsCentrifugeOptical fiberMaterials scienceContext (archaeology)Geotechnical engineeringStrain gaugeFiber optic sensorPressure sensorIsotropyStructural health monitoringLateral earth pressureOpticsStructural engineeringAcousticsComposite materialEngineeringFiberGeologyMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

Optical strain sensing for structural health monitoring is sometimes deployed for field and laboratory study of civil structures. Rayleigh backscatter devices (ROFDR) are presented for use with geotechnical centrifuge research since they offer distributed sensing capabilities, and through this study are shown to have negligible interference from external pressure effects. A comparison between a single channel and multi-channel fibre optic rotary joint (FORJ) is presented in the context of transmitting optical strain data across a rotating interface. The orthogonal pressure effects of a free-floating fibre under isotropic pressure was <0.32 με/kPa and that the pressure effect on a fibre bonded to a metal surface was below the detection limit of the instrument, 1 με, for an applied pressure of 60 kPa. The ROFDR system showed highly repeatable measurement of a constant temperature reading in a water bath experiment. The system is stable to ±10 microstrain within 2-sigma for a >12 h constant temperature test. An example case of a pipeline buried in a slope experiencing a landslide is presented using fiber pairs to capture pipe stress / strain. Geotechnical centrifuge modelling in a 1 m drum was carried out using a multi-channel FORJ coupled with an ROFDR system.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.536

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 integrity0.0000.001
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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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