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Record W3120424706 · doi:10.1007/s42947-020-6003-8

The potential use of lightweight cellular concrete in pavement application: a review

2020· review· en· W3120424706 on OpenAlexaffabout
Frank Mi-Way Ni, Abimbola Grace Oyeyi, Susan Tighe

Bibliographic record

VenueInternational Journal of Pavement Research and Technology · 2020
Typereview
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSubbaseSubgradeService lifePavement engineeringDurabilityCivil engineeringEngineeringMaterials scienceMechanical engineeringComposite materialAsphaltMathematics

Abstract

fetched live from OpenAlex

Abstract Protecting the pavement subgrade to increase the service life of road pavements is an aspect currently being explored. Several alternative pavement subbase materials are being considered, including Lightweight Cellular Concrete (LCC). Due to its lower weight, LCC incorporating industrial by-product, making it sustainable, and ease of use amongst other benefits, is seen as a potential candidate. This paper reports reviewing the potential application of LCC within the pavement structure with a specific application as a subbase. It examines the various properties such as modulus of elasticity, compressive and tensile strength, Water absorption, and freeze-thaw resistance necessary for pavement application. It also assesses its use in the field in Canada considering the design methods utilized. Some limitations and gaps for LCC application in pavements are also established and recommendations on how to further its use and performance. This review concludes that LCC possesses potential as a pavement subbase alternative; however, other mechanical properties like LCC’s fatigue life is essential. A comparative field study is also recommended to monitor actual performance and various factors on performance.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.354
Teacher spread0.300 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations24
Published2020
Admission routes2
Has abstractyes

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