Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer

Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer

This study used three artificial intelligence-based algorithms – adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) – to develop empirical models for predicting the compressive strength (CS) and slump values of fly ash-based geopol...

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Bibliographic Details
Main Authors: Sohaib Nazar, Jian Yang, Muhammad Nasir Amin, Kaffayatullah Khan, Muhammad Ashraf, Fahid Aslam, Mohammad Faisal Javed, Sayed M. Eldin
Format: Article
Language:English
Published: Elsevier 2023-05-01
Series:Journal of Materials Research and Technology
Subjects:
Geopolymer concrete
Compressive strength
Slump
Artificial intelligence techniques
Machine learning algorithms (MLA)
Artificial neural networks
Online Access:http://www.sciencedirect.com/science/article/pii/S2238785423004143

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http://www.sciencedirect.com/science/article/pii/S2238785423004143

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