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Record W4392123675 · doi:10.1139/cjce-2023-0448

Seismic performance assessment of post-tensioned CLT shear wall buildings with buckling-restrained axial fuses

2024· article· en· W4392123675 on OpenAlexafffundvenueabout
Huanru Zhu, Matiyas A. Bezabeh, Asif Iqbal, Marjan Popovski, Zhiyong Chen

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsFPInnovationsUniversity of Northern British ColumbiaMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural engineeringBucklingShear wallShear (geology)EngineeringGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Post-tensioned cross-laminated timber (PT-CLT) walls have been demonstrated to be a low-damage seismic force-resisting system (SFRS) due to their self-centering capability. However, there is still a need to examine the seismic performance of such SFRS in high-seismic risk zones. This study evaluates the seismic performance of 6-, 9-, and 12-storey PT-CLT shear wall buildings in Vancouver, Canada, equipped with buckling-restrained axial fuses. The prototype buildings were designed using the displacement-based design method, and the assessment considered the most recent seismic hazard model provided in the 2020 National Building Code of Canada. To conduct nonlinear response history analysis (NLRHA) and incremental dynamic analysis (IDA), numerical models were developed in OpenSeesPy and calibrated based on component- and system-level experimental tests. The NLRHA and IDA results demonstrate that all the studied buildings have adequate collapse margin ratios, with less than a 10% chance of collapsing at the maximum considered earthquakes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.193
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations10
Published2024
Admission routes4
Has abstractyes

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