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Record W4390038042 · doi:10.1139/cjce-2023-0257

Seismic vulnerability assessment of post-tensioned timber building fitted with dissipative bracing systems

2023· article· en· W4390038042 on OpenAlexafffundvenue
Ikenna Odikamnoro, Prakash S. Badal, Solomon Tesfamariam

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of WaterlooOkanagan CollegeOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBracingVulnerability (computing)Dissipative systemStructural engineeringVulnerability assessmentGeotechnical engineeringEngineeringForensic engineeringGeologyCivil engineeringComputer scienceBracePhysicsPsychologyComputer security

Abstract

fetched live from OpenAlex

A post-tensioned (PT) timber building coupled with dissipative bracing systems is a sustainable seismic-resistant structural system. This system combines desirable qualities of self-centering, energy dissipation, and lightweight timber, which attracts lesser seismic force and contributes to a better resilient system. Despite significant advancement in the provision of these sustainable seismic-resistant timber-based structural archetypes, more study is required to derive a fuller knowledge of the behaviour of these innovative structural systems under a range of ground motion intensities and limited state capacities. Direct displacement-based design, a performance-based design approach, is used for the design of this system, and performance assessment is carried out using nonlinear dynamic analysis. This study investigates the performance of the braced PT frame under different limit state considerations using ground motion records consistent with the NBC 2020 seismic hazard.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.220
Teacher spread0.212 · 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
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

Citations3
Published2023
Admission routes3
Has abstractyes

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