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Record W2946759396 · doi:10.1139/cjce-2018-0300

Responses and capacity curves of mid- and high-rise wood buildings subjected to seismic excitations

2019· article· en· W2946759396 on OpenAlexaffvenueabout
Shu Yang, Han Hong, F. Michael Bartlett

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsWestern University
Fundersnot available
KeywordsStructural engineeringStiffnessEngineeringNonlinear systemCross laminated timberSeismic analysisIncremental Dynamic AnalysisGeotechnical engineeringYield (engineering)Structural loadMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The design and modelling of 10-, 15-, and 20-storey wood buildings with cross-laminated timber and glulam structural members are presented in the present study. The system is platform-type construction with a cross-laminated timber core, perimeter walls as lateral load resisting system, and glulam columns as the gravity system. The designed buildings satisfy requirements stipulated in applicable design codes in Canada. The hysteretic models and the associated parameters used to model the fasteners for wood members are developed. Assessments of the nonlinear inelastic seismic responses and capacity curves of the designed buildings are carried out using the incremental dynamic analysis (IDA) and nonlinear static pushover analysis (NSPA) methods. The results indicate that the NSPA curve closely approximates the mean capacity curve estimated using the IDA curves for wood buildings. The post-yield stiffness ratio to initial stiffness for the wood buildings ranges from approximately 0.35 to 0.55. The results also show that the effect of the record-to-record variability on the IDA curves is substantial.

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 score0.612
Threshold uncertainty score0.507

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 head0.008
GPT teacher head0.169
Teacher spread0.161 · 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

Citations12
Published2019
Admission routes3
Has abstractyes

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