Evidence map›Paper›PMID 41330987›Full record

ArticleScientific reports2025

Prediction and parametric modeling of compressive strength of waste marble dust concrete through machine learning and experimental analysis.

Abdullah Alzlfawi, Md Habibur Rahman Sobuz, Md Kawsarul, Islam Kabbo, Mita Khatun, Sani Aliyu Abubakar, M Jameel, Md Jihad Miah

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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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

8 authors.

Abdullah AlzlfawiDepartment of Civil and Environmental Engineering, College of Engineering, Majmaah University, Al Majmaah, 11952, Saudi Arabia. a.alzlfawi@mu.edu.sa.ORCID http://orcid.org/0009-0000-5797-1164
Md Habibur Rahman SobuzDepartment of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh. habib@becm.kuet.ac.bd.ORCID http://orcid.org/0000-0002-6279-5406
Md KawsarulDepartment of Civil and Environmental Engineering, College of Engineering, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
Islam KabboDepartment of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh.
Mita KhatunDepartment of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh.
Sani Aliyu AbubakarDepartment of Civil Engineering, Kampala International University, Western Campus, Ishaka -Bushenyi, Western Region, Kampala, Uganda. saliyu@kiu.ac.ug.ORCID http://orcid.org/0000-0001-7682-9745
M JameelDepartment of Civil Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia.
Md Jihad MiahDepartment of Architecture, Technology and Engineering, University of Brighton, Lewes Road, Brighton, BN2 4GJ, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The production of waste marble is the ultimate concern of the construction industry's recent success in satisfying the huge need for marble stones. This study predicts the compressive strength of waste marble powder concrete using machine learning methods XG Boost, AdaBoost, Cat-Boost, Gradient-Boosting, Light Gradient-Boosting, and decision tree. For this purpose, a comprehensive dataset was developed from previously published literature, incorporating input features such as cement, fine and coarse aggregates, superplasticizers, silica fume, marble dust, and water, with compressive strength (CS) as the output parameter. Based on the outcomes, XG Boost model outperforms other models to predict CS with R

Indexed as

Compressive strengthMachine learningParametric modeling.Strength predictionWaste marble dust

Identifiers

PMID41330987
PMCPMC12673063

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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.