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Smart Structures and Systems
  Volume 33, Number 1, January 2024 , pages 65-91
DOI: https://doi.org/10.12989/sss.2024.33.1.065
 

Slime mold and four other nature-inspired optimization algorithms in analyzing the concrete compressive strength
Yinghao Zhao, Hossein Moayedi, Loke Kok Foong and Quynh T. Thi

 
Abstract
    The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMAMLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R2) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.
 
Key Words
    fly ash; high strength concrete; neural-evolutionary; optimization
 
Address
(1) Yinghao Zhao:
School of Civil Engineering and Engineering Management, Guangzhou Maritime University, Guangzhou, 510725, China;
(2) Hossein Moayedi, Loke Kok Foong, Quynh T. Thi:
Institute of Research and Development, Duy Tan University, Da Nang, Vietnam;
(3) Hossein Moayedi, Loke Kok Foong, Quynh T. Thi:
School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam.
 
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