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Record W2277141106 · doi:10.1139/cjce-2015-0467

Finite element model calibration of multi-story buildings through interferometric imaging and spectral ratio method

2016· article· en· W2277141106 on OpenAlexaffvenue
Yavuz Kaya

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInterferometryCalibrationFinite element methodRoofStiffnessOpticsFourier transformAmplitudeCoupling (piping)Structural engineeringComputer scienceRemote sensingAcousticsGeologyPhysicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

To identify and calibrate the finite element model for multi-story buildings, a simplifıed approach is being introduced in this paper; this methodology is based on interferometric imaging and encompasses spectral ratio (SR) method. Interferometric imaging provides a new set of seismic data free of coupling between structures to its subsurface and independent of the excitation of the building. The SR method, on the other hand, is the ratio of Fourier amplitude spectra of the roof-deconvolved records on two adjacent stories. The newly introduced methodology enables each story to be identified uniquely for its stiffness since the SR of a particular story is dependent only on the properties of itself and the stories above.

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: Methods · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.021
GPT teacher head0.264
Teacher spread0.243 · 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
GenreMethods

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

Citations1
Published2016
Admission routes2
Has abstractyes

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