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Record W2186940353

Exposure classes for designing durable concrete

2009· article· en· W2186940353 on OpenAlexaboutno aff
Vijay. R. Kulkarni

Bibliographic record

VenueIndian Concrete Journal · 2009
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsnot available
Fundersnot available
KeywordsLimitingDurabilitySustainabilityForensic engineeringConstruction engineeringEuropean standardEngineeringComputer scienceCivil engineeringArchitectural engineeringRisk analysis (engineering)BusinessMechanical engineeringEcology
DOInot available

Abstract

fetched live from OpenAlex

Recent years have witnessed numerous cases of premature deterioration of reinforced concrete structures. Simultaneously, the urgent need to inculcate sustainability approach in the design and construction of structures has come to the forefront. As a result, durability design provisions in standards of many countries including India have become more stringent. The present paper describes some of the latest durability-centric provisions in the Australian, European, North American and Canadian standards, mainly highlighting the changes in the definitions of exposure classes and the limiting values of the properties of concrete for different classes. With a view to align the provisions of Indian Standard IS 456:2000 to the international trend, the paper suggests changes in the existing definitions of exposure classes of this standard. These definitions have been expanded and made more rational by aligning them to the anticipated degradation mechanisms. Limiting values of concrete properties are suggested for the new exposure classes. While doing so, an attempt has been made to keep the limiting values more or less similar to those in the existing IS 456:2000.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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 designTheoretical or conceptual
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

Citations14
Published2009
Admission routes1
Has abstractyes

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