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Record W4413016976 · doi:10.22215/etd/2025-16604

Effect of Redundancy on Seismic Performance of Braced Frame Buildings in Eastern and Western Canada

2025· dissertation· en· W4413016976 on OpenAlexaboutno aff
Mostafa Toopchinezhad

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRedundancy (engineering)Braced frameFrame (networking)SeismologyGeologyEngineeringTelecommunicationsReliability engineering

Abstract

fetched live from OpenAlex

Structural redundancy plays a critical role in the seismic performance of buildings. However, in Canada, it is not explicitly addressed in design codes, and other standards, such as ASCE 7-22, apply uniform redundancy factors without accounting for system-specific variations. This study investigates redundancy effects in chevron braced steel frame buildings using nine five-storey archetypes with varying bracing layouts and configurations, designed for Montreal and Vancouver. Archetypes were analyzed in OpenSees using pushover and incremental dynamic analyses (IDA). Pushover results showed that redundancy, particularly through additional braced bays, improved overstrength. IDA results revealed similar trends at the Life Safety and Collapse Prevention levels, while Immediate Occupancy was more influenced by brace quantity than layout. Regression analyses confirmed strong correlations between performance metrics and structural parameters. This work presents a performance-based framework for redundancy assessment to support refinement of seismic design standards and evaluation procedures in Canada and the United States.

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)0.002
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.002
GPT teacher head0.195
Teacher spread0.193 · 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
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractno

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