Evidence map›Paper›PMID 41298842›Full record

ArticleScientific reports2025

Microstructural, mechanical characterization and ANN prediction of concrete with glass fiber roving waste.

Yasin Onuralp Özkılıç, Sadık Alper Yıldızel, Muhannad Riyadh Alasiri, Ali İhsan Çelik, Mohamed Abdikarin Mohamud

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yasin Onuralp ÖzkılıçDepartment of Civil Engineering, Faculty of Engineering, Necmettin Erbakan University, Konya, Turkey. yozkilic@erbakan.edu.tr.
Sadık Alper YıldızelDepartment of Civil Engineering, Engineering Faculty, Karamanoglu Mehmetbey University, 70200, Karaman, Turkey.
Muhannad Riyadh AlasiriCivil Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia.
Ali İhsan ÇelikDepartment of Construction, Tomarza Mustafa Akincioglu Vocational School, Kayseri University, 38940, Kayseri, Turkey.
Mohamed Abdikarin MohamudDepartment of Civil Engineering, Faculty of Engineering, Necmettin Erbakan University, Konya, Turkey. mohamedabdikarim@zust.edu.so.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents an experimental and computational framework for the production of sustainable concrete reinforced with glass fiber roving waste (GFRW). This study combines laboratory testing, microstructural analysis, and artificial neural network (ANN) modeling to assess and enhance the mechanical properties of GFRW-reinforced concrete. A total of 145 samples were cast using different mix proportions and fiber conditions, with fiber contents of 1%, 2%, and 3% and lengths of 3, 6, 9, and 12 mm. The mixtures containing 6 mm fibers at about 1-2% volume fraction showed the most balanced performance, producing clear gains in compressive, tensile, and flexural strength compared with the control mix. The ANN model-trained with five-fold cross-validation-achieved strong predictive accuracy (R

Indexed as

Artificial neural networkConcreteFiber reinforced concreteGlass fiberWaste

Identifiers

PMID41298842
PMCPMC12748587

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.