Evidence map›Paper›PMID 40594120›Full record

ArticleScientific reports2025

Data-driven framework for prediction of mechanical properties of waste glass aggregates concrete.

Kennedy C Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M Ebid, Hamza Imran, Miguel Angel Duque Vaca, Greys Carolina Herrera Morales, Nestor Ulloa, Krishna Prakash Arunachalam

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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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Kennedy C OnyeloweDepartment of Civil Engineering, College of Eng & Eng Technology, Michael Okpara University of Agriculture, Umudike, Nigeria. kennedychibuzor@kiu.ac.ug.
Shadi HanandehDepartment of Civil Engineering, al-Balqa Applied University, As-Salt, Jordan.
Viroon KamchoomExcellent Center for Green and Sustainable Infrastructure, Department of Civil Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang (KMITL), Bangkok, 10520, Thailand. viroon.ka@kmitl.ac.th.
Ahmed M EbidDepartment of Civil Engineering, Faculty of Engineering, Future University in Egypt, New Cairo, Egypt.
Hamza ImranDepartment of Environmental Science, College of Energy and Environmental Science, Al-Karkh University of Science, Baghdad, 10081, Iraq. hamza.ali1990@kus.edu.iq.
Miguel Angel Duque VacaFacultad de Informática y Electrónica, Escuela Superior Politécnica de Chimborazo (ESPOCH), Panamericana Sur km. 1½, 060155, Riobamba, Ecuador.
Greys Carolina Herrera MoralesEscuela Superior Politécnica de Chimborazo (ESPOCH), Sede Orellana, Ecuador.
Nestor UlloaFacultad de Mécanica, Escuela Superior Politécnica de Chimborazo (ESPOCH), 060155, Riobamba, Ecuador. nestor.ulloa@espoch.edu.ec.
Krishna Prakash ArunachalamDepartamento de Ciencias de la Construcción, Facultad de Ciencias de la Construcción Ordenamiento Territorial, Universidad Tecnológica Metropolitana, Santiago, Chile.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research presents a novel data-driven framework for predicting the mechanical properties of waste glass aggregate concrete using six advanced metaheuristic optimization algorithms: Bat Algorithm (Bat), Cuckoo Search Algorithm (Cuckoo), Elephant Herding Optimization (Elephant), Firefly Algorithm (Firefly), Rhinoceros Optimization Algorithm (Rhino), and Gray Wolf Optimizer (Wolf). The study evaluates these models based on their ability to predict compressive strength (Fc), tensile strength (Ft), density, and slump using key statistical performance indicators such as SSE, MAE, MSE, RMSE, accuracy, R

Indexed as

Mechanical propertiesMetaheuristic machine learningSensitivity analysisSustainable constructionWaste glass aggregate concrete

Identifiers

PMID40594120
PMCPMC12219597

What OpenQuestion holds

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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.