Comparison of deep LSTM and machine learning models for predicting compressive strength of fly ash/slag-based geopolymer concrete.

Ceren Kina, Harun Tanyildizi, Mohd Mustafa Al Bakri Abdullah, Rafiza Abdul Razak, Thanongsak Imjai

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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. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Ceren KinaFaculty of Engineering and Natural Sciences, Department of Civil Engineering, Malatya Turgut Ozal University, Malatya, Türkiye.

Harun TanyildiziTechnology Faculty, Department of Civil Engineering, Firat University, Elazig, Türkiye.

Mohd Mustafa Al Bakri AbdullahCenter of Excellence Geopolymer & Green Technology (CEGeoGTech), Universiti Malaysia Perlis (UniMAP), Perlis, 01000, Malaysia. mustafa_albakri@unimap.edu.my.

Rafiza Abdul RazakFaculty of Civil Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Perlis, 01000, Malaysia.

Thanongsak ImjaiSchool of Engineering and Technology, Walailak University, 222 Thaiburi, Thasala, 80160, Nakhon Si Thammarat, Thailand.

Funding

European Commission 689857-PRIGeoC-RISE-2015

6 · The paper itself

Abstract

In the production of geopolymer concrete (GPC), using ground granulated blast furnace slag (GGBFS) and fly ash (FA) can reduce the carbon dioxide footprint and decrease the amount of waste materials released into the environment. Finding the compressive strength (fc) of GPC through experiments is time-consuming and costly; thus, applying artificial intelligence models can expedite this process. This study aims to compare the performance of deep Long Short-Term Memory (LSTM) and the machine learning (ML)-based algorithms in predicting the f

Indexed as

Compressive strengthFly ashGeopolymer concreteGround granulated blast furnace slagMachine learning

Identifiers

PMID40998878

DOI10.1038/s41598-025-14365-6

PMCPMC12464153

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LicenceCC BY

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