Predicting the compressive strength of self‐compacting concrete containing Class F fly ash using metaheuristic radial basis function neural network

The use of Class F fly ash (CFFA) as a partial replacement of cement in the concrete mixture can provide a wide variety benefits such as improving the mechanical properties, reducing the construction costs, and enhancing the environmental conditions. Compressive strength as one of the most critical...

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Bibliographic Details
Published in:Structural concrete : journal of the FIB 2022-04, Vol.23 (2), p.1191-1213
Main Authors: Pazouki, Gholamreza, Golafshani, Emadaldin Mohammadi, Behnood, Ali
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Class F fly ash
Compressive strength
Construction costs
firefly optimization algorithm
Fly ash
Heuristic methods
Mathematical models
Mechanical properties
Neural networks
Optimization
Parameters
Performance measurement
Prediction models
Radial basis function
radial basis function neural network
Self-compacting concrete
Superplasticizers
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Summary:The use of Class F fly ash (CFFA) as a partial replacement of cement in the concrete mixture can provide a wide variety benefits such as improving the mechanical properties, reducing the construction costs, and enhancing the environmental conditions. Compressive strength as one of the most critical mechanical parameters used in many design codes can be obtained either through costly and time‐consuming experiments or non‐destructive methods such as predictive models. In this study, a hybrid model of the radial basis function neural network (RBFNN) and firefly optimization algorithm (FOA) (RBFNN + FOA) was proposed for the prediction of the compressive strength of self‐compacting concrete (SCC) containing CFFA. For this purpose, a multi‐laboratory dataset containing 327 SCC samples made with CFFA was employed. The input parameters in the proposed models included the age of the specimen and the amounts of cement, water, CFFA, coarse aggregate, fine aggregate, and superplasticizer, and the only output parameter was the compressive strength of SCC. For comparison purpose, the artificial neural network (ANN) was also used to model the compressive strength of SCC containing CFFA. The results show that the compressive strength estimated by the proposed and ANN models have good accuracies compared to the experimental results. Moreover, several performance metrics showed that the accuracy of the proposed model is higher than that of the model developed based on ANN algorithm.
ISSN:1464-4177
1751-7648
DOI:10.1002/suco.202000047