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Validation and application of analytical design approach for multi-storey platform-type CLT shear walls

2025· article· en· W4406411504 on OpenAlexaff
Mohammad Masroor, Thomas Tannert

Bibliographic record

VenueEngineering Structures · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsStructural engineeringCross laminated timberShear wallShear (geology)Model validationEngineeringComputer scienceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Mass timber products in general and cross-laminated timber (CLT) in particular, have garnered significant attention in the construction industry due to their structural and environmental benefits. Extensive efforts have been undertaken to investigate the lateral behavior of CLT shear walls , involving experimental tests and analytical developments. However, experimental validation of analytical models for calculating the resistance and lateral deformation of multi-storey platform-type CLT shear walls are not available. To close this gap, two-storey shear wall models, developed using the calibrated properties from connection-level experimental tests, were validated with the results from two-storey CLT shear wall tests. Subsequently, analytical models for six-storey CLT shear walls were developed to investigate the behaviour of connections and their contributions to the lateral behaviour of mid-rise CLT shear wall systems. A capacity-based design procedure was successfully implemented, ensuring proper capacity distribution along the building height. The significant influences of aspect ratio and tension strap design on inter-storey drift and shear wall resistance was highlighted. Additionally, with proper design, rocking behavior dominated, and the bending deformations were limited to less than 30 %, meeting the current design requirements.

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: Methods · Consensus signal: none
Teacher disagreement score0.647
Threshold uncertainty score0.722

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 head0.021
GPT teacher head0.260
Teacher spread0.239 · 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
GenreMethods

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

Citations4
Published2025
Admission routes1
Has abstractyes

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