Evidence map›Paper›PMID 42342892›Full record

ArticleScientific reports2026

Predicting the strength of waste aggregate concrete blocks using novel hybrid machine learning models and graphical user interface deployment.

Md Arifuzzaman, S M Shishir Ahmed, Md Abdul Khaled, Sameen Yasar Siddiq, Prajnat Barua, Sourov Paul, Md Mahamudul Hasan, A K M Azad, Muhammad Ali Martuza, Ayed Eid Alluqmani

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

10 authors.

Md ArifuzzamanDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University, P.O. Box 380, 31982, Al-Ahsa, Kingdom of Saudi Arabia. marifuzzaman@kfu.edu.sa.
S M Shishir AhmedDepartment of Civil Engineering, Military Institute of Science and Technology (MIST), Dhaka, Bangladesh.
Md Abdul KhaledDepartment of Civil Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka, Bangladesh.
Sameen Yasar SiddiqDepartment of Civil Engineering, Military Institute of Science and Technology (MIST), Dhaka, Bangladesh.
Prajnat BaruaDepartment of Civil and Environmental Engineering, North South University (NSU), Dhaka, Bangladesh.
Sourov PaulDepartment of Civil Engineering, Leading University, Sylhet, 3112, Bangladesh.
Md Mahamudul HasanDepartment of Civil Engineering, Military Institute of Science and Technology (MIST), Dhaka, Bangladesh.
A K M AzadDepartment of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), 13318, Riyadh, Kingdom of Saudi Arabia.
Muhammad Ali MartuzaDepartment of Computer Engineering, College of Computer, Qassim University, 51452, Buraydah, Kingdom of Saudi Arabia.
Ayed Eid AlluqmaniDepartment of Civil Engineering, Faculty of Engineering, Islamic University of Madinah, Prince Naif Ibn Abdulaziz, 42351, Medina, Kingdom of Saudi Arabia. aayed@iu.edu.sa.

Funding

Deanship of Scientific Research, King Faisal University KFU261259
6 · The paper itself

Abstract

Concrete blocks made from waste aggregates have become a promising way to reduce waste and conserve natural resources while still offering good mechanical performance in both solid and hollow concrete blocks. This is especially relevant as more sustainable construction projects increasingly use recycled and alternative materials. This research develops a series of novel hybrid machine learning (ML) models to accurately predict compressive strength, using a dataset of 544 concrete samples from various sources. The six novel hybrid ML frameworks are designed as Hybrid Stacked Ensemble (HSE), Hybrid Residual Learning (HRL), Hybrid Weighted Ensemble (HWE), Hybrid Meta-Learning (HML), Hybrid Bayesian Stacking (HBS), and Hybrid Feature Fusion (HFF). Results show that novel Hybrid Bayesian Stacking (HBS) algorithms deliver excellent predictive accuracy across all evaluation metrics, with (R

Indexed as

Compressive strength predictionConcrete blockGUI deploymentHybrid Bayesian stacking ensembleIndividual conditional expectationSHAP interpretabilitySustainable constructionWaste aggregate

Identifiers

PMID42342892
PMCPMC13350711

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LicenceCC BY-NC-ND
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Registered trials

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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.