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Record W615160747

New Life Expectancy. Self-curing Concrete from Canada and Long-lived Bridge Decks

2011· article· en· W615160747 on OpenAlexaboutno aff
John Latta, Tina Grady Barbaccia, Mike Anderson

Bibliographic record

VenueBetter roads · 2011
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
Fundersnot available
KeywordsCrackingShrinkageCorrosionReinforcementGeotechnical engineeringCuring (chemistry)Materials scienceForensic engineeringEnvironmental scienceStructural engineeringEngineeringComposite material
DOInot available

Abstract

fetched live from OpenAlex

This article describes a more durable concrete that will increase the average lifespan of bridge decks by more than 20 years compared to typical high-strength concrete, and by more than 40 years compared to normal-strength concrete. This high-performance concrete has been specially formulated to minimize shrinkage, which is typical of high-strength concrete, while maintaining its excellent mechanical properties. It also greatly reduces cracking, which diminishes the penetration of aggressive agents into the concrete, such as chlorides from the de-icing salts used on roads. As a result, it takes considerably more time for the chlorides to reach the steel reinforcement, initiate corrosion and induce further damage to the structure. The key difference is in the sand: lightweight, porous, shale-fine aggregate, which replaces about a quarter of the normal sand used to make concrete. This porous sand can hold up to 20 percent of its own weight of water, which serves to cure the concrete uniformly from the inside, thus preventing self-desiccation. With a unit cost only 5 percent higher than that of a standard high-strength concrete, Cusson expects concrete bridge decks made with this new concrete to last longer, saving taxpayers money in annual bridge maintenance, recurring repairs and associated traffic disruption, and replacement.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.190
Teacher spread0.171 · 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.

Study designObservational
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
Published2011
Admission routes1
Has abstractyes

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