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Record W2036167553 · doi:10.1680/coma.900064

Lightweight concrete incorporating volcanic materials

2012· article· en· W2036167553 on OpenAlexaff
Khandaker M. Anwar Hossain

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Construction Materials · 2012
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPumiceMaterials scienceDurabilityShrinkageCompressive strengthUltimate tensile strengthPortland cementComposite materialVolcanic ashMercury intrusion porosimetryCementGeotechnical engineeringVolcanoPorosityGeologyPorous medium

Abstract

fetched live from OpenAlex

This paper describes properties of lightweight concrete obtained by incorporating volcanic-materials-based blended cements and pumice aggregates. Blended cements are produced by replacing ASTM Type I Portland cement with 20% volcanic ash and finely ground pumice. Properties of fresh lightweight concrete mixtures (such as slump and air content) along with their mechanical properties (such as density, compressive/tensile strength and modulus of elasticity) are described. The durability and microstructural characteristics are investigated by drying shrinkage, water permeability, mercury intrusion porosimetry, differential scanning calorimetry and microhardness tests. The investigation suggests that volcanic-materials-based blended cements in combination with pumice aggregates can be used for the production of lightweight concrete for structural applications, having satisfactory strength and durability characteristics. The use of volcanic ash/pumice-based blended cements induces the beneficial effect of reducing drying shrinkage and water permeability as well as refinement of pore structures and better interfacial transition zone. Development and use of such inexpensive and environmentally friendly lightweight concretes can be extremely helpful for the sustainable development and rehabilitation of volcanic disaster areas around the world.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.032
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.209
Teacher spread0.199 · 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.

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

Citations11
Published2012
Admission routes1
Has abstractyes

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Same venueProceedings of the Institution of Civil Engineers - Construction MaterialsSame topicConcrete and Cement Materials ResearchFrench-language works237,207