Neural Network for Mixture Design Optimization of Geopolymer Concrete

The paper discusses the development and application of the artificial neural network (ANN) model for predicting the compressive and splitting tensile strength of the geopolymer-based concrete composites (GPC). The strength properties of GPC are influenced by the proportions of the constituents--name...

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Bibliographic Details
Published in:ACI materials journal Vol. 118; no. 4; pp. 91 - 96
Main Authors: Bhogayata, Ankur, Kakadiya, Sneh, Makwana, Rinkesh
Format: Journal Article
Language:English
Published: Farmington Hills American Concrete Institute 01.07.2021
Subjects:
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
Back propagation
Back propagation networks
Caustic soda
Cement
Chemical properties
Compressive strength
Concrete
Constituents
Data collection
Data processing
Datasets
Design optimization
Fly ash
Fuzzy logic
Geopolymers
Literature reviews
Mathematical optimization
Mechanical properties
Neural networks
Operators (mathematics)
Optimization techniques
Tensile strength
Variables
India
ISSN:0889-325X, 0889-325X, 1944-737X
Online Access:Get full text
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Summary:The paper discusses the development and application of the artificial neural network (ANN) model for predicting the compressive and splitting tensile strength of the geopolymer-based concrete composites (GPC). The strength properties of GPC are influenced by the proportions of the constituents--namely, the alkaline solution, fly ash, aggregates, and sand and water--and require optimization for the desired quality of the composite. The optimum mixture may be obtained by using modern techniques; namely ANN modeling. The ANN models have been developed by training and validating the input data using the sigmoid function and the feed-forward backpropagation algorithm in the hidden layers. The ANN layer is the functional part of the model consisting of the operators to carry out the specific task largely based on mathematical calculations. A five-layered ANN model has been developed and used to predict the strength to optimize the mixture design. The predicted values have been compared with the experimental strength values, and the effects of the most significant constituents have been studied. Keywords: artificial neural network (ANN); compressive strength; geopolymer concrete; optimization; splitting tensile strength.
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ISSN:0889-325X
0889-325X
1944-737X
DOI:10.14359/51732711