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Record W7093322592 · doi:10.1016/j.istruc.2025.109914

Cyclic strength degradation of shallow cast-in-place anchor bolts modeled using continuous surface cap plasticity

2025· article· en· W7093322592 on OpenAlexaff

Bibliographic record

VenueStructures · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBrittlenessDissipationSlabParametric statisticsPlasticityMonotonic functionExponentStrength reduction

Abstract

fetched live from OpenAlex

The cyclic-to-monotonic pullout strength degradation of shallow cast-in-place anchors is investigated using a continuous surface cap plasticity model within a numerically developed and experimentally validated framework. The parametric investigation considers variations in the effective depth-to-diameter ratio, concrete type, and loading regime. Results indicate that anchors with an effective depth-to-diameter ratio less than five exhibit brittle failure modes and the lowest energy dissipation capacity, whereas deeper anchors demonstrate more ductile behavior. While depth-to-diameter exponent is estimated as 1.353 for monotonic regime, cyclic pullout leads to a reduced size effect sensitivity suggesting 1.237 exponent. Moreover, introducing a top layer of steel reinforcement in the concrete slab mitigates brittle fracture size effects, as reflected by 1.219 exponent in the depth-dependent strength expression. An average cyclic strength reduction factor of 0.75 is recommended for shallow cast-in-place anchors.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.247
Teacher spread0.236 · 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 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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