Evidence map›Paper›PMID 39916833›Full record

ArticleHeliyon2025

Machine learning for predicting strength properties of waste iron slag concrete.

Matiur Rahman Raju, Syed Ishtiaq Ahmad, Md Mehedi Hasan, Noor Md Sadiqul Hasan, Md Monirul Islam, Md Abdul Basit, Ishraq Tasnim Hossain, Saif Ahmed Santo, Md Shahrior Alam, Mahfuzur Rahman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 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

10 authors.

Matiur Rahman RajuDepartment of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh.
Syed Ishtiaq AhmadDepartment of Civil Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
Md Mehedi HasanDepartment of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh.
Noor Md Sadiqul HasanDepartment of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh.
Md Monirul IslamDepartment of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh.
Md Abdul BasitDepartment of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh.
Ishraq Tasnim HossainDepartment of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh.
Saif Ahmed SantoDepartment of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh.
Md Shahrior AlamLocal Government Engineering Department (LGED), LGED Bhaban, Dhaka, 1207, Bangladesh.
Mahfuzur RahmanDepartment of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the utilization of waste iron slag (WIS) as a sustainable alternative in concrete production to reduce environmental impact and preserve natural resources. The experimental investigation of WIS-incorporated concrete focused on compressive and tensile strength with machine learning (ML) models for prediction. Among the tested ML algorithms, Decision Tree (DT) and XGBoost showed the highest accuracy (R

Indexed as

Compressive strengthConcreteCost analysisMachine learningSplit tensile strengthWaste iron slag

Identifiers

PMID39916833
PMCPMC11795792

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.