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Record W2121016293 · doi:10.1117/12.548117

<title>Civionics specifications for fiber optic sensors for structural health monitoring</title>

2004· article· en· W2121016293 on OpenAlexaff
Evangeline Rivera, Aftab A. Mufti, D. J. Thomson

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsStructural health monitoringFiber Bragg gratingProcess (computing)Computer scienceEngineeringFiber optic sensorOptical fiberSystems engineeringTelecommunicationsElectrical engineeringOperating system

Abstract

fetched live from OpenAlex

As the design and construction of civil structures continue to evolve, it is becoming imperative that these structures be monitored for their health. In order to meet this need, the discipline of Civionics has emerged. It involves the applications to civil structures and aims to assist engineers in realizing the full benefits of structural health monitoring (SHM). Therefore, the goal of the specification outlined in this work is to ensure that correct installation and operating of fiber optic sensors, such as bridges, will be discussed that motivated the writing of these specifications. The main reason for the failure of FOS based monitoring systems can be traced directly to the installation of the fiber sensor itself. Therefore, by creating a standard procedure for SHM, several ambiguities are eliminated such as fiber sensor specifications and the types of cables required. As a result, these specifications will help ensure that the sensors will survive the installation process and eventually prove their value over years of monitoring the health of the structure. The Civionics FOS specifications include the requirements for fiber sensors, specifically Bragg grating sensors, and their corresponding readout unit. It also includes specifications on the cables, conduits, junction boxes, cable termination and the environmental.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score0.955

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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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 designTheoretical or conceptual
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

Citations4
Published2004
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Fiber Optic SensorsFrench-language works237,207