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Record W7115724635 · doi:10.71846/18-wcee-2456

SEISMIC PERFORMANCE EVALUATION OF BALLOON-TYPE CONVENTIONAL AND RESILIENT CLT SHEAR WALLS

2025· article· en· W7115724635 on OpenAlexaboutno aff

Bibliographic record

VenueWorld Conference of Earthquake Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsShear wallFinite element methodCross laminated timberShear (geology)Nonlinear systemResponse spectrum

Abstract

fetched live from OpenAlex

This paper evaluates and compares the seismic performance of prototype modular mass timber buildings with balloon-type conventional and resilient cross laminated timber (CLT) shear walls as seismic force-resisting systems (SFRS). The newly developed dual-pinned self-centering coupled wall has been employed as a resilient SFRS, while conventional balloon-type CLT walls with doweled hold downs have been studied as the conventional SFRS. The seismic performance of buildings has been evaluated by employing the performance-based evaluation procedure outlined in the technical guideline published by the Canadian Construction Materials Center (CCMC)/National Research Council Canada (NRC). Robust finite element models of the resilient and conventional CLT shear walls have been developed in OpenSees. The nonlinear behavior of the displacement-controlled components is calibrated using available experimental data. A suite of ground motions suitable for the site located in Vancouver has been selected, scaled, and subjected to numerical models at 100% and 200% uniform hazard spectrum (UHS) intensity levels according to CCMC/NRC procedure. The results highlight the difference in the damage level and performance of the studied systems.

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 score0.176
Threshold uncertainty score0.506

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 head0.029
GPT teacher head0.223
Teacher spread0.194 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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