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Record W2781508117 · doi:10.1139/cjce-2017-0107

Wind-induced shear and torsion in low-rise and medium-rise buildings: Provisions of National Building Code of Canada 2015

2018· article· en· W2781508117 on OpenAlexafffundvenueabout
Thai‐Son Nguyen, Ted Stathopoulos, Lucia Tirca

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsConcordia University
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsTorsion (gastropod)Structural engineeringShear (geology)EngineeringGeotechnical engineeringMaterials scienceComposite material

Abstract

fetched live from OpenAlex

This paper discusses the shear and torsion induced in low-rise and medium-rise buildings, according to wind load specifications provided in NBCC 2015. Results from experimental studies, carried out in wind tunnels were compared with corresponding NBCC 2015 provisions under different upstream roughness conditions. These comparisons demonstrated notable discrepancies for the case of torsion in low-rise buildings. Further, comparisons between the wind load specifications given in NBCC 2015 and ASCE/SEI 7-10 standard were carried out. Following both sets of provisions, wind-induced shear and torsion were computed and compared for five low-rise and medium-rise buildings with the same horizontal dimensions but different heights. Emphasis was directed towards the cases that create maximum shear forces and (or) maximum torsions to reflect critical design conditions. For low-rise buildings, the ASCE/SEI-7-10 and NBCC-2015 yield similar shear coefficients but quite different torsional coefficients; while for medium-rise buildings, clear agreement was found, for both shear and torsion. The diversity of the results is discussed and some suggestions for improvement of code provisions are made. A definition for medium-rise buildings was provided.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.932

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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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 designObservational
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

Citations1
Published2018
Admission routes4
Has abstractyes

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