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Record W2906149133 · doi:10.32920/ryerson.14648574.v1

Structural health monitoring of two-way slabs based on random decrement technique

2021· preprint· en· W2906149133 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

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSlabStructural engineeringStructural health monitoringCrackingDeflection (physics)VibrationMaterials scienceComputer scienceAcousticsEngineeringComposite materialPhysicsOptics

Abstract

fetched live from OpenAlex

The current research attempts to explore the feasible use of a Structural Health Monitoring method for a two-way slab system through the effective vibration based damage diagnostic technique of Random Decrement (RD). Experimental investigations have been conducted on a total of four reinforced concrete two-way slab specimens. The slabs behaviour was examined under static loading. The results were presented in terms of load-deflection relationship at service and ultimate load, crack pattern and failure modes. At each stage of loading, the ambient vibration excitation test has been performed to investigate the extent of damage at the cracking, yield, and ultimate states through changes in dynamic parameters obtained from RD signatures. Additional applications of RD technique were performed on two-way slabs, first, to explore the location of damage by Multi-Channel Random Decrement using FBG sensor arrays. Secondly, RD technique was utilized to evaluate the extent of damage under successive equal dynamic impacts.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.287
Teacher spread0.270 · 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

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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