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Record W2913715231 · doi:10.1142/s021945541950055x

A New Damage Index for Isolated Structures

2019· article· en· W2913715231 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

VenueInternational Journal of Structural Stability and Dynamics · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNoise (video)Structural engineeringPrincipal component analysisStructural health monitoringComputer scienceFrame (networking)Base (topology)MathematicsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Damage indices based on structural dynamic characteristics are often used to detect damage in the structures. In this study, a new index for identifying damages in base-isolated structures is proposed using the frequency response function (FRF). Since calculation of the FRF data is time- and memory-consuming for problems of large size, the two-dimensional principal component analysis technique is employed to decrease the data size. The damage indices calculated, representing the health state of the structure, are stored in a database, which are then used to detect the damage location and severity by utilizing the lookup table method. The proposed damage detection method is applied to four concrete frame models, one of which is fixed at the base and the others are isolated by elastomeric bearings. The FRF data are polluted with three different noise values (5%, 10% and 15%) in order to evaluate the uncertainty of measurements. The accuracy of the proposed indices is compared with each other for various parameters such as noise values, bearings characteristics, base conditions and different damage scenarios. The results show the precision of the proposed method.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.491

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 head0.010
GPT teacher head0.279
Teacher spread0.269 · 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