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Record W4386157876 · doi:10.32920/24034116.v1

Development of a Test Method to Evaluate Durability of Concrete Control Joints Against De-Icer Salts

2023· preprint· en· W4386157876 on OpenAlexaff
Ramy Elbakari

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDurabilityJoint (building)Flexural strengthStructural engineeringWettingMaterials scienceComposite materialEngineering

Abstract

fetched live from OpenAlex

This thesis introduces a test method that can be used to evaluate the integrity of control joints. Different salt concentrations and exposure conditions are tested including freeze-thaw cycles, wet-dry cycles and a combination thereof. The test sample is composed of a square block measuring 200x200x120 mm and a joint running in the middle at 40 mm depth. It was revealed that, for accelerated damage to be induced, the test method needs to include two salts with two conditions. The first exposure uses NaCl at 10% concentration with 50 cycles of freeze/thaw- wet/dry alternating every 5 consecutive cycles. The second exposure uses CaCl2 at 15% concentration with 50 cycles of wetting and drying at 5°C. Damage assessment is carried out using two approaches: 1) strength loss under flexural loading and 2) visual damage. The proposed method needs more optimization but is a step in the direction of testing different configuration of joints and concrete properties.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.034
GPT teacher head0.304
Teacher spread0.270 · 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
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

Citations0
Published2023
Admission routes1
Has abstractyes

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