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Proposing two new metaheuristic algorithms of ALO-MLP and SHO-MLP in predicting bearing capacity of circular footing located on horizontal multilayer soil

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Abstract

In this study, for the issue of shallow circular footing’s bearing capacity (also shown as Fult), we used the merits of artificial neural network (ANN), while optimized it by two metaheuristic algorithms (i.e., ant lion optimization (ALO) and the spotted hyena optimizer (SHO)). Several studies demonstrated that ANNs have significant results in terms of predicting the soil’s bearing capacity. Nevertheless, most models of ANN learning consist of different disadvantages. Accordantly, we focused on the application of two hybrid models of ALO–MLP and SHO–MLP for predicting the Fult placed in layered soils. Moreover, we performed an Extensive Finite Element (FE) modeling on 16 sets of soil layer (soft soil placed onto stronger soil and vice versa) considering a database that consists of 703 testing and 2810 training datasets for preparing the training and testing datasets. The independent variables in terms of ALO and SHO algorithms have been optimized by taking into account a trial and error process. The input data layers consisted of (i) upper layer foundation/thickness width (h/B) ratio, (ii) bottom and topsoil layer properties (for example, six of the most important properties of soil), (iii) vertical settlement (s), (iv) footing width (B), where the main target was taken Fult. According to RMSE and R2, values of (0.996 and 0.034) and (0.994 and 0.044) are obtained for training dataset and values of (0.994 and 0.040) and (0.991 and 0.050) are found for the testing dataset of proposed SHO–MLP and ALO–MLP best-fit prediction network structures, respectively. This proves higher reliability of the proposed hybrid model of SHO–MLP in approximating shallow circular footing bearing capacity.

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Acknowledgements

Financial support from the Fundamental Research Funds for the Central Universities (No.FRF-TP-18-015A3) is gratefully acknowledged.

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Authors and Affiliations

  1. School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Wensheng Liu

  2. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  3. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  4. Department of Surface Mining, Hanoi University of Mining Land Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam

    Hoang Nguyen

  5. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam

    Zongjie Lyu

  6. Department of Business and IT, University of South-Eastern Norway, Bø i Telemark, 3800, Notodden, Norway

    Dieu Tien Bui

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  1. Wensheng Liu
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  4. Zongjie Lyu
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  5. Dieu Tien Bui
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Correspondence to Hossein Moayedi.

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Corresponding author at: Ton Duc Thang University, Ho Chi Minh City, Vietnam.

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Liu, W., Moayedi, H., Nguyen, H. et al. Proposing two new metaheuristic algorithms of ALO-MLP and SHO-MLP in predicting bearing capacity of circular footing located on horizontal multilayer soil. Engineering with Computers 37, 1537–1547 (2021). https://doi.org/10.1007/s00366-019-00897-9

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