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Record W4396508135 · doi:10.22215/etd/2023-15931

The Lateral and Post-Impact Residual Lateral Strength of Reinforced Concrete Columns

2023· dissertation· en· W4396508135 on OpenAlexaff
Mickey Brayden George

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsCarleton University
Fundersnot available
KeywordsResidualMaterials scienceImpact resistanceDeformation (meteorology)Structural engineeringReinforced concreteComposite materialReinforcementResidual strengthImpact energyGeotechnical engineeringGeologyMathematicsEngineering

Abstract

fetched live from OpenAlex

Lateral load resistance tests were conducted on 24 concrete columns with various reinforcement configurations and sizes by the author. Of these specimens, 13 were previously subjected to impact loading by Abdallah et al. and were used to evaluate the post-impact performance and lateral load capacity retention. From these tests it was determined that the CFST specimens exhibited both the largest lateral and residual lateral capacities of 37.37 kN and 40.17 kN, respectively. The CFST member had the highest retention of lateral load capacity for both low and high impact energy, retaining 75% and 107% of the capacity of the unimpacted specimen, respectively. Increasing the specimen diameter had no discernible effect on lateral strength retention but reduced the residual deformation especially at high impact energies with the residual deformation being reduced as much as 85% compared to the smaller specimen.

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)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.005
GPT teacher head0.245
Teacher spread0.240 · 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
Published2023
Admission routes1
Has abstractyes

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