MétaCan
Menu
Back to cohort

Field Monitoring of RC-Structures under Dynamic Loading Using Distributed Fiber-Optic Sensors

2020· article· en· W3027572657 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Performance of Constructed Facilities · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsQueen's University
Fundersnot available
KeywordsStructural health monitoringComputer scienceOptical fiberField (mathematics)Structural engineeringFiber optic sensorEngineeringTelecommunications

Abstract

fetched live from OpenAlex

The ability to properly assess existing reinforced concrete (RC) structures provides an opportunity to prevent costly rehabilitations or replacements and aid in the optimization of future designs. Distributed fiber-optic sensing (DFOS) is a promising option in the assessment of these complex structures. Previous uses of this technology, however, have been limited to static measurements, prohibiting the assessment of structures exposed to dynamic loads. This research intends to assess the dynamic sensing capabilities of a DFOS system and, in turn, improve the current understanding of the dynamic behavior of an existing RC beam tested in situ through a case study. Based on the data provided by the dynamic distributed fiber-optic sensors (DDFOS), dynamic measurements of distributed strains, distributed deflections, and crack widths can all be provided. Further details regarding the use of the DDFOS system in assessment, including the determination of support conditions and dynamic response factors, are presented and 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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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