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Record W2315897030 · doi:10.1139/l2012-047

Identification of the dynamic properties of a reinforced concrete coupled shear wall residential high-rise building

2012· article· en· W2315897030 on OpenAlexaffvenueabout
Natthapong Areemit, Michael Montgomery, Constantin Christopoulos, Agha Hasan

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsOccupational Cancer Research CentreUniversity of Toronto
Fundersnot available
KeywordsShear wallStructural engineeringVibrationDamping ratioHigh riseAmbient vibrationWind engineeringReinforced concreteEngineeringFinite element method

Abstract

fetched live from OpenAlex

As high-rise buildings increase with height and slenderness, they become increasingly sensitive to dynamic vibrations, and therefore the natural frequency of vibration and damping ratio are very important design parameters, as they directly impact the design wind forces. Recent advances in sensing and computing technology have made it possible to monitor the dynamic behaviour of full-scale structures, which was not possible in the past. Full-scale validation of the dynamic properties is useful for high-rise designers to verify design assumptions, especially since recent measurements have shown that damping decreases as the height of the building increases, and in situ damping measurements have been lower than many currently assumed design values, potentially leading to unconservative designs. A 50-storey residential building in downtown Toronto, with a reinforced concrete coupled shear wall lateral load resisting system with outriggers was monitored using current state-of-the-art sensing technologies and techniques to determine, in situ, the dynamic properties under real wind loads. The in situ measurements were then compared with results obtained using current state-of-the-art computer modelling techniques.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.466

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations6
Published2012
Admission routes3
Has abstractyes

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