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Record W2087139239 · doi:10.1139/l06-159

Civionics for structural health monitoring

2007· article· en· W2087139239 on OpenAlexvenueno aff
E. Rivera, Aftab A. Mufti, D. J. Thomson

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsnot available
Fundersnot available
KeywordsStructural health monitoringProcess (computing)Key (lock)EngineeringElectronicsConstruction engineeringWork (physics)Systems engineeringComputer scienceRisk analysis (engineering)Computer securityMechanical engineeringElectrical engineering

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. To meet this need, the discipline of civionics has emerged. It involves the application of electronics to civil structures and aims to assist engineers in realizing the full benefits of structural health monitoring (SHM). Therefore, the goal of the civionics specifications outlined in this work is to ensure that the installation and operation of fibre optic sensors are successful. This paper will discuss several lessons learned during the implementation of health monitoring systems for civil structures. The monitoring of these structures primarily motivated the writing of these specifications. Creating a standard procedure for SHM eliminated several ambiguities, such as fibre sensor specifications and the types of cables required. As a result, it is expected that these specifications will help ensure that the sensors will survive the installation process and eventually prove their value over years of structural health monitoring. The civionics fibre optic sensor specifications include the requirements for fibre sensors and their corresponding readout units. They also include specifications for the cables, conduits, junction boxes, termination, and environmental protection.Key words: civionics, structural health monitoring, fibre optic sensors, specifications.

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: none
Teacher disagreement score0.704
Threshold uncertainty score0.713

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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

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