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Record W4415210387 · doi:10.1061/jpcfev.cfeng-4759

Collapse Behavior of Platform-Type CLT Construction Using Shake-Table Experimental Tests

2025· article· en· W4415210387 on OpenAlexaff
Hiroshi Isoda, Motoshi Sato, Kazuyuki Matsumoto, Tatsuya Miyake, Takafumi Nakagawa, Solomon Tesfamariam

Bibliographic record

VenueJournal of Performance of Constructed Facilities · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDissipationCross laminated timberSeismic analysisShear wallLimit state designShear (geology)Rigidity (electromagnetism)Seismic loading

Abstract

fetched live from OpenAlex

Cross-laminated timber (CLT) walls generally have higher rigidity and seismic resistance capacity, and it was predicted that the CLT building height restrictions could be relaxed in regions with higher seismic hazards. The collapse of CLT buildings is not well understood because research that investigates the limit state performance is scant. In this paper, incremental shake-table tests were conducted on two full-scale 2-story platform-type narrow-panel CLT buildings, W1 and W2, to investigate the collapse limit state. W1 featured two 1 m wide CLT shear walls per story, whereas W2 had a single 2 m wide CLT shear wall per story. The response of W1 was governed by rocking walls, whereas W2 was dominated by sliding at higher seismic intensities. Neither W1 nor W2 collapsed at the seismic excitation of JMA Kobe NS 140%, at which the maximum interstory drift ratios of 8.8% and 5.9% were achieved. The system has sufficient energy dissipation mechanisms, but the need for an overall system-level design that considers innovative connections is apparent.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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 designBench or experimental
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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