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Record W7115701791 · doi:10.71846/18-wcee-1876

NUMERICAL STUDY ON PERFORMANCE OF PRECAST SHEAR WALL CONNECTION UNDER SHEAR-TENSION INTERACTION LOAD

2025· article· en· W7115701791 on OpenAlexaboutno aff

Bibliographic record

VenueWorld Conference of Earthquake Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPrecast concreteShear wallStiffnessConnection (principal bundle)WeldingShear (geology)Ductility (Earth science)Tension (geology)Structural load

Abstract

fetched live from OpenAlex

Precast concrete shear walls have attracted many engineers and researchers because of various advantages, such as enhanced quality control, shortened construction time, and reduced environmental impact compared to conventional construction methods. However, their connection method, which must provide adequate strength, ductility, and structural integrity under various loads, is an essential concern in these systems. In addition, the seismic performance of precast concrete shear wall structures depends on the behavior and continuity of the connections between the walls. This paper investigated an existing vertical connection of precast shear walls in Canada using welded plates designed for a low seismic zone. The primary purpose is to improve the nonlinear behavior of these types of connections by adding some steel components like end plates and bent rebars. The numerical modeling using DIANA FEA is calibrated based on an experimental study on welded plate connection between two precast concrete panels under shear and tension loads. Then, the pushover curves of the connections are evaluated under pure tension, pure shear, and shear-tension loads to suggest some methods for enhancing structural response, including ultimate capacity, ductility, energy dissipation, and failure mode. The results showed that using end plates and bent rebars in these connections increased the ductility and post-yield deformation. However, the stiffness and strength did not change considerably.

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 score0.117
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.234
Teacher spread0.211 · 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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