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A Combined Genetic Algorithm-Artificial Neural Network Optimization Method for Mix Design of Self Consolidating Concrete

Amirhossein Tahmouresi 1, Amir Robati 1, Girum Urgessa 2, and Homa Haghighi 2
1. Islamic Azad University, Kerman, Iran
2. George Mason University, Fairfax, VA, USA

Manuscript October 7, 2020; revised January 5, 2021; accepted March 18, 2021; issue published July 16, 2021.

Abstract— The use of intelligent optimization and modeling methods is rapidly increasing in many fields including concrete technology. In recent years, concrete mix design has been studied using intelligent models in which the artificial neural networks are among the most popular and widely utilized method. However, this modeling depends on an optimization process, and the structured model should be tuned by implementing optimization techniques. Additionally, finding the most appropriate neural network structure for solving the concrete mix design problem was proven to be an important challenge in the state-of-the art. Therefore, this paper introduces a novel strategy in which an evolutionary algorithm and a structure of artificial neural network were fused to find the best network for modeling the compressive strength of Self Consolidating Concrete (SCC) and to extract the most optimal mix design. The novel strategy is tested using 169 data-sets with each set containing 11 concrete constituent properties. The proposed GA-ANN-GA strategy not only finds the best model but also presents the most optimal mix design of concrete to mitigate the challenges reported in recent studies.

Index Terms—optimization, modeling, artificial neural network, concrete mix design, evolutionary algorithm

Cite: Amirhossein Tahmouresi, Amir Robati, Girum Urgessa, and Homa Haghighi, "A Combined Genetic Algorithm-Artificial Neural Network Optimization Method for Mix Design of Self Consolidating Concrete," International Journal of Structural and Civil Engineering Research, Vol. 10, No. 3, pp. 106-112, August 2021. doi: 10.18178/ijscer.10.3.106-112

​Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.