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Record W4308875874 · doi:10.3390/su142214752

The Effects of the Type and Quantity of Recycled Materials on Physical and Mechanical Properties of Concrete and Mortar: A Review

2022· review· en· W4308875874 on OpenAlexaff
Yassine Brahami, Ali Saeidi, M. Fiset, Kadiata Ba

Bibliographic record

VenueSustainability · 2022
Typereview
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMortarReuseCementAggregate (composite)Work (physics)Waste managementEnvironmental scienceProperties of concreteCivil engineeringMaterials scienceEngineeringComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

The reuse of industrial wastes to produce concrete and mortar is an environmental solution for their disposal as well as for the development of ecological and sustainable concrete. A large number of previous studies summarized in this review paper focused on adding different types of waste in the concrete and mortar mix in the form of fine aggregates, coarse aggregates or cement additives, and investigated the physical and mechanical properties of the enhanced material. Reusing waste in concrete and mortar mix design significantly affects the material’s fresh and hardened properties. This literature review offers a general insight to the civil and industrial engineering community on ecological waste-based concrete and mortar that can serve as a basis for construction and future work in this field.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.270
Teacher spread0.254 · 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 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

Citations8
Published2022
Admission routes1
Has abstractyes

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