Smart Structures and Systems

  • 제25권6호
  • /
  • Pages.765-778
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  • 2020
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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

Hybridized dragonfly, whale and ant lion algorithms in enlarged pile's behavior

  • Ye, Xinyu (School of Civil Engineering, Central South University) ;
  • Lyu, Zongjie (Institute of Research and Development, Duy Tan University) ;
  • Foong, Loke Kok (Department for Management of Science and Technology Development, Ton Duc Thang University)
  • 투고 : 2019.11.28
  • 심사 : 2020.02.14
  • 발행 : 2020.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

The present study intends to find a proper solution for the estimation of the physical behaviors of enlarged piles through a combination of small-scale laboratory tests and a hybrid computational predictive intelligence process. In the first step, experimental program is completed considering various critical influential factors. The results of the best multilayer perceptron (MLP)-based predictive network was implemented through three mathematical-based solutions of dragonfly algorithm (DA), whale optimization algorithm (WOA), and ant lion optimization (ALO). Three proposed models, after convergence analysis, suggested excellent performance. These analyses varied based on neurons number (e.g., in the basis MLP hidden layer) and of course, the level of its complexity. The training R2 results of the best hybrid structure of DA-MLP, WOA-MLP, and ALO-MLP were 0.996, 0.996, and 0.998 where the testing R2 was 0.995, 0.985, and 0.998, respectively. Similarly, the training RMSE of 0.046, 0.051, and 0.034 were obtained for the training and testing datasets of DA-MLP, WOA-MLP, and ALO-MLP techniques, while the testing RMSE of 0.088, 0.053, and 0.053, respectively. This obtained result demonstrates the excellent prediction from the optimized structure of the proposed models if only population sensitivity analysis performs. Indeed, the ALO-MLP was slightly better than WOA-MLP and DA-MLP methods.

키워드

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