A water cycle-based error minimization technique in predicting the bearing capacity of shallow foundation

Engineering with Computers (2021)Cite this article

Abstract

Selecting the appropriate training technique is a significant step in utilizing intelligent approaches. It becomes even more important when it comes to critical problems like analyzing the bearing capacity of foundations. This study investigates the feasibility of a capable metaheuristic algorithm, called the water cycle algorithm (WCA), for training a multi-layer perceptron (MLP). The WCA-MLP is applied to a large finite element dataset to predict the settlement. The results of this model are compared with electromagnetic field optimization (EFO) and shuffled complex evolution (SCE) benchmarks. With reference to the obtained Pearson correlation factors (larger than 0.88 in all stages), all employed models are suitable for the mentioned objective. Moreover, it was observed that the training error of the WCA was 5.84 and 3.89% smaller than the EFO and SCE, respectively. Likewise, the accuracy of the WCA-MLP was 1.85 and 2.04% larger in the testing phase. Also, a predictive equation is finally elicited for practical applications in compatible circumstances.

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Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Hossein Moayedi

  2. Faculty of Civil Engineering, Duy Tan University, Da Nang, 550000, Vietnam

    Hossein Moayedi

  3. Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Amirhosein Mosavi

  4. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Amirhosein Mosavi

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  1. Hossein Moayedi
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  2. Amirhosein Mosavi
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Correspondence to Hossein Moayedi.

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Moayedi, H., Mosavi, A. A water cycle-based error minimization technique in predicting the bearing capacity of shallow foundation. Engineering with Computers (2021). https://doi.org/10.1007/s00366-021-01289-8

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Keywords

  • Bearing capacity
  • Settlement measurement
  • Artificial neural network
  • Water cycle algorithm
  • Machine learning
  • Artificial intelligence