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Record W3093545791 · doi:10.1155/2020/7687039

Long‐Term Monitoring Reliability and Life Prediction of Fiber Bragg Grating‐Based Self‐Sensing Steel Strands

2020· article· en· W3093545791 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

VenueAdvances in Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsGeomechanica (Canada)
FundersNational Natural Science Foundation of China
KeywordsReliability (semiconductor)Fiber Bragg gratingMaterials scienceStructural engineeringCorrosionUltimate tensile strengthSensitivity (control systems)Composite materialEngineeringElectronic engineering

Abstract

fetched live from OpenAlex

To analyze the long‐term monitoring reliability and life expectancy of FBG‐based steel strands, accelerated corrosion and tensile tests were carried out and a life‐prediction model was constructed. The validation test results indicated that the monitoring strain sensitivity of FBG‐based steel strands decreases with an increase in solution concentration and time in a corrosive acidic environment. When the sensitivity dropped to about 80% of its initial value, the FBG sensor suddenly failed. The life‐prediction model indicates that the predicted monitoring life of an FBG sensor is about 56 years in an unstressed condition but about 27 years under the stressful conditions that FBG‐based steel strands are subjected to in their working environment. So, to improve their monitoring reliability and monitoring life, it is suggested that FBG‐based steel strands might be prepared by “pre‐loading.”

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 categoriesMeta-epidemiology (narrow)
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 score0.049
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.009
GPT teacher head0.219
Teacher spread0.209 · 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