Ant Colony Optimization-Driven Ensemble Learning for Carbon Emission Modelling in Fly Ash-Slag Geopolymer Concrete.

Indra Kumar Pandey, Sanjay Kumar, Brajkishor Prasad, Pramod Kumar, Mizan Ahmed, Ardalan B Hussein

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 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

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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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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Indra Kumar PandeyDepartment of Civil Engineering, National Institute of Technology, Jamshedpur 831013, India.

Sanjay KumarDepartment of Civil Engineering, National Institute of Technology, Jamshedpur 831013, India.

Brajkishor PrasadDepartment of Civil Engineering, National Institute of Technology, Jamshedpur 831013, India.ORCID 0000-0003-1099-2122

Pramod KumarDepartment of Civil Engineering, Graphic Era Deemed to be University, Dehradun 248002, India.

Mizan AhmedCentre for Infrastructure Monitoring and Protection, School of Civil and Mechanical Engineering, Curtin University, Kent Street, Bentley, WA 6102, Australia.ORCID 0000-0001-5499-3181

Ardalan B HusseinDepartment of Structural Engineering and Geotechnics, Széchenyi István University, Egyetem Tér 1, 9026 Győr, Hungary.ORCID 0000-0001-8623-7794

Funding

Széchenyi István University 141PTP2026

6 · The paper itself

Abstract

This study investigates the prediction of carbon emissions from fly ash and ground granulated blast furnace slag-based geopolymer concrete (GPC) using advanced ensemble machine learning (ML) techniques. Although ML has been extensively utilized to model GPC's mechanical performance, its application in estimating environmental impacts, specifically carbon emissions, is limited. The research employs six ensemble ML models, such as random forest, gradient boosting, extreme gradient boosting (XGB), CatBoost, and light gradient boosting machine (LGBM), including versions optimized using ant colony optimization (ACO). Among them, the ACO-enhanced XGB model demonstrated the highest predictive accuracy with a coefficient of determination (R

Indexed as

carbon emissionsensemble learningfly ashgeopolymer concreteground granulated blast furnace slagmachine learning

Identifiers

PMID42195807

DOI10.3390/ma19102168

PMCPMC13208860

What OpenQuestion holds

Textmetadata

LicenceCC BY

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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.