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Record W4412754791 · doi:10.11159/iccste25.243

Characterizing Concrete Performance Mixed with Coarse Aggregate Sourced from Northern Saudi Arabia: A Case Study

2025· article· en· W4412754791 on OpenAlexvenueno aff
Nasser Alanazi

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAggregate (composite)Computer scienceMaterials scienceComposite material

Abstract

fetched live from OpenAlex

The coarse aggregate particles, which are a main ingredient in concrete, are typically obtained from crushed rocks or natural deposits such as riverbeds or valleys.This research aimed to explore the effectiveness of using local crushed aggregate as well as local valley aggregate as coarse aggregate in producing concrete.Several standard tests were conducted to explore the physical properties of both aggregate types, and a large number of concrete samples were prepared to test the properties of concrete made with crushed or valley aggregates.The finding revealed that the crushed aggregate exhibits higher bulk density, specific gravity, and lower water absorption compared to the valley aggregate.Additionally, the workability of fresh concrete made with valley aggregate was better than that of concrete mixed with crushed aggregate, due to the roundness and smoothness of the valley aggregate.However, concrete containing crushed aggregate exhibited better compressive and split tensile strength due to the naturally rough surface and shape angularity.Finally, the study concluded that both sources of coarse aggregate are suitable for use in concrete production, with concrete incorporating crushed aggregate slightly stronger and concrete made with valley aggregate having better workability.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.770

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)0.0000.000

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 head0.010
GPT teacher head0.201
Teacher spread0.191 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the International Conference on Civil, Structural and Transportation EngineeringSame topicRecycled Aggregate Concrete PerformanceFrench-language works237,207