Innovative modeling techniques including MEP, ANN and FQ to forecast the compressive strength of geopolymer concrete modified with nanoparticles

The use of nano-materials to improve the engineering properties of different types of concrete composites including geopolymer concrete (GPC) has recently gained popularity. Numerous programs have been executed to investigate the mechanical properties of GPC. In general, compressive strength (CS) is...

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Bibliographic Details
Published in:Neural computing & applications Vol. 35; no. 17; pp. 12453 - 12479
Main Authors: Ahmed, Hemn Unis, Mohammed, Ahmed S., Faraj, Rabar H., Abdalla, Aso A., Qaidi, Shaker M. A., Sor, Nadhim Hamah, Mohammed, Azad A.
Format: Journal Article
Language:English
Published: London Springer London 01.06.2023
Springer Nature B.V
Subjects:
Artificial Intelligence
Artificial neural networks
Cement
Civil engineering
Compressive strength
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Concrete mixing
Curing
Data Mining and Knowledge Discovery
Engineering schools
Geopolymers
Image Processing and Computer Vision
Machine learning
Mechanical properties
Modelling
Nanoparticles
Neural networks
Original Article
Polymerization
Probability and Statistics in Computer Science
Sensitivity analysis
Statistical analysis
Variables
Compressive strength
Sensitivity analysis
Artificial neural network
Multi-expression programming
Modeling
Geopolymer concrete
ISSN:0941-0643, 1433-3058
Online Access:Get full text
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Summary:The use of nano-materials to improve the engineering properties of different types of concrete composites including geopolymer concrete (GPC) has recently gained popularity. Numerous programs have been executed to investigate the mechanical properties of GPC. In general, compressive strength (CS) is an essential mechanical indicator for judging the quality of concrete. Traditional test methods for determining the CS of GPC are expensive, time-consuming and limiting due to the complicated interplay of a wide variety of mixing proportions and curing regimes. Therefore, in this study, artificial neural network (ANN), multi-expression programming, full quadratic, linear regression and M5P-tree machine learning techniques were used to predict the CS of GPC. In this instance, around 207 tested CS values were extracted from the literature and studied to promote the models. During the process of modeling, eleven effective variables were utilized as input model parameters, and one variable was utilized as an output. Four statistical indicators were used to judge how well the models worked, and the sensitivity analysis was carried out. According to the results, the ANN model calculated the CS of GPC with greater precision than the other models. On the other hand, the ratio of alkaline solution to the binder, molarity, NaOH content, curing temperature and concrete age have substantial effects on the CS of GPC.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08378-3