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Record W4409211493 · doi:10.1061/9780784486085.035

Design Guidance for Point-Supported CLT Floors

2025· article· en· W4409211493 on OpenAlexaff
Houman Ganjali, Md Shahnewaz, Carla Dickof, Thomas Tannert

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis of Composite Materials
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsPoint (geometry)Computer scienceMathematicsGeometry

Abstract

fetched live from OpenAlex

Cross-laminated timber (CLT) has gained popularity in recent years as a sustainable and cost-effective alternative to traditional construction materials. The crosswise layup in CLT provides two-way resistance, which makes CLT a suitable choice for point-supported applications. In this system, the panels are directly supported by columns, eliminating the need for beams and their connections, resulting in an increased free story height. In such applications, CLT punching shear resistance is one of the key properties and is directly related to the rolling shear strength of the lamellas. The factors influencing CLT punching shear resistance are either material-strength related or support-condition related and should be accounted for when designing point supported CLT floors. This study provides the results of 164 punching shear tests conducted on CLT panels sized 1.7 × 1.8 m, 1.5 × 1.8 m, and 1.5 × 1.5 m, identifying the impact of column location; CLT provider, lamella species and grade; and support geometry. Adopting an appropriate shear stress distribution model is crucial for punching shear design of CLT floors; therefore, a comparison between the results from the plane beam equation, shear analogy method, and transformed composite section method is presented in this study.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.832
Threshold uncertainty score0.397

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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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 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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