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Record W2124809526 · doi:10.1139/t04-059

Measurement of pile load transfer using the Fiber Bragg Grating sensor system

2004· article· en· W2124809526 on OpenAlexvenueno aff
Woojin Lee, Won-Je Lee, Sang Bae Lee, Rodrigo Salgado

Bibliographic record

VenueCanadian Geotechnical Journal · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsnot available
Fundersnot available
KeywordsFiber Bragg gratingPileInstrumentation (computer programming)Optical fiberFiber optic sensorPrecast concreteStrain gaugeMaterials scienceOpticsStructural engineeringEngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

A series of laboratory and field tests were performed to evaluate the applicability of an optical fiber sensor system in the instrumentation of piles. A multiplexed sensor system, constructed by arranging several Fiber Bragg Grating (FBG) sensors along a single line of optical fiber, is capable of measuring local axial strains as a function of wavelength shifts. The distributions of axial load in three model piles and a field test pile evaluated from the strains measured by FBG sensors are found to be comparable, in terms of both magnitude and trend, with those obtained from conventional strain gauges. This suggests that the FBG sensor system is an effective tool for the analysis of the axial load transfer in piles. The successful instrumentation of a soil–cement injected precast (SIP) pile using FBG sensors suggests that the use of these sensors in drilled shafts and other types of cast in situ concrete piles is feasible. With the rapid advance of optical fiber sensor technology, the economics of the use of optical fiber sensors in this type of instrumentation is expected to improve significantly in coming years.Key words: pile foundation, load transfer, fiber optic sensor, Fiber Bragg Grating sensor.

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 score0.388
Threshold uncertainty score0.652

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 head0.020
GPT teacher head0.210
Teacher spread0.190 · 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

Citations79
Published2004
Admission routes1
Has abstractyes

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