Smart Structures and Systems

  • 제33권1호
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  • Pages.65-91
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  • 2024
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Slime mold and four other nature-inspired optimization algorithms in analyzing the concrete compressive strength

  • Yinghao Zhao (School of Civil Engineering and Engineering Management, Guangzhou Maritime University) ;
  • Hossein Moayedi (Institute of Research and Development, Duy Tan University) ;
  • Loke Kok Foong (Institute of Research and Development, Duy Tan University) ;
  • Quynh T. Thi (Institute of Research and Development, Duy Tan University)
  • 투고 : 2020.09.09
  • 심사 : 2023.12.28
  • 발행 : 2024.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

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 SMA-MLP, 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.

키워드

과제정보

This study was supported by Guangdong Provincial Key Laboratory of Green Construction and Intelligent Operation & Maintenance for Off- shore Infrastructure, and supported by Special Projects in Key Fields of Higher Education in Guangdong Province (No. 2023ZDZX4044).

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