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Record W4249063843 · doi:10.32920/ryerson.14646999.v1

A comparative study on the performance of different types of wallslab connections in liquid containing concrete structures

2021· preprint· en· W4249063843 on OpenAlexaff
Nima Ahangar Atashi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSlabCrackingStructural engineeringJoint (building)Finite element methodMonotonic functionMaterials scienceEngineeringComposite materialMathematics

Abstract

fetched live from OpenAlex

Waterstops are commonly used at cold joints in construction of liquid containing concrete structures. A common issue with application of waterstops, is their conflict with top layer of slab reinforcements. A common solution is to form an upturn part that raises the waterstop clear of the top slab bars. However, effects of these different types of construction joints on the performance of structures have not been investigated. To that end, full-scale wall-slab specimens, each representing a different type of construction joint, are built and tested under different monotonic and cyclic loadings. The performance of these specimens is compared with regards to first cracking, limit state capacity and leakage. Results of the tests show that conventional flat joint, behaves more rigidly compared to other types of joints. A non-linear finite element analysis of a typical wall-slab specimen, is also presented and its results are compared with the results of the experiments.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.037
GPT teacher head0.278
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
Published2021
Admission routes1
Has abstractyes

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