Liang ChenThis email address is being protected from spambots. You need JavaScript enabled to view it.

Chongqing University of Posts and Telecommunications, Chongqing, 400065, China


 

Received: January 19, 2022
Accepted: July 2, 2022
Publication Date: October 14, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202307_26(7).0010  


ABSTRACT


This study aims to develop a surrogate artificial neural network-based technique for predicting the compressive strength of concrete, which is the most significant factor in the life service of concrete and its durability in civil construction projects of civil engineering. For this goal, a data set for high-performance concrete is gathered from the literature repository, which includes a different percentage of fly ash, silica fume, and superplasticizer in the mix designs. The data set is applied to train and validate the optimal structured artificial neural network optimized by innovative equilibrium optimization and particle swarm optimization algorithms. The results showed that the EOANN-I and PANN-I model with the R2 and RMSE values of 0.9889,1.771, 0.9881, and 1.8813, stood at the first and second stage of the most capable models in the prediction of HPC compressive strength. Although the PANN-I model’s complexity is lower than the EOANN-I model, the rate of its prediction accuracy covers its lack of complexity.


Keywords: HPC concrete; compressive strength; artificial neural network; equilibrium optimization algorithm; particle swarm optimization algorithm


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