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Effective Hybrid Soft Computing Approach for Optimum Design of Shallow Foundations

Author

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  • Mohammad Khajehzadeh

    (Department of Civil Engineering, Anar Branch, Islamic Azad University, Anar 7741943615, Iran)

  • Suraparb Keawsawasvong

    (Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, Thailand)

  • Moncef L. Nehdi

    (Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4M6, Canada)

Abstract

In this study, an effective intelligent system based on artificial neural networks (ANNs) and a modified rat swarm optimizer (MRSO) was developed to predict the ultimate bearing capacity of shallow foundations and their optimum design using the predicted bearing capacity value. To provide the neural network with adequate training and testing data, an extensive literature review was used to compile a database comprising 97 datasets retrieved from load tests both on large-scale and smaller-scale sized footings. To refine the network architecture, several trial and error experiments were performed using various numbers of neurons in the hidden layer. Accordingly, the optimal architecture of the ANN was 5 × 10 × 1. The performance and prediction capacity of the developed model were appraised using the root mean square error (RMSE) and correlation coefficient (R). According to the obtained results, the ANN model with a RMSE value equal to 0.0249 and R value equal to 0.9908 was a reliable, simple and valid computational model for estimating the load bearing capacity of footings. The developed ANN model was applied to a case study of spread footing optimization, and the results revealed that the proposed model is competent to provide better optimal solutions and to outperform traditional existing methods.

Suggested Citation

  • Mohammad Khajehzadeh & Suraparb Keawsawasvong & Moncef L. Nehdi, 2022. "Effective Hybrid Soft Computing Approach for Optimum Design of Shallow Foundations," Sustainability, MDPI, vol. 14(3), pages 1-20, February.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1847-:d:743017
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    References listed on IDEAS

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    1. Yılmaz Delice & Emel Kızılkaya Aydoğan & Uğur Özcan & Mehmet Sıtkı İlkay, 2017. "A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 23-36, January.
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    Cited by:

    1. Hamed Safayenikoo & Fatemeh Nejati & Moncef L. Nehdi, 2022. "Indirect Analysis of Concrete Slump Using Different Metaheuristic-Empowered Neural Processors," Sustainability, MDPI, vol. 14(16), pages 1-16, August.
    2. Abdul Ghani Olabi & Hegazy Rezk & Mohammad Ali Abdelkareem & Tabbi Awotwe & Hussein M. Maghrabie & Fatahallah Freig Selim & Shek Mohammod Atiqure Rahman & Sheikh Khaleduzzaman Shah & Alaa A. Zaky, 2023. "Optimal Parameter Identification of Perovskite Solar Cells Using Modified Bald Eagle Search Optimization Algorithm," Energies, MDPI, vol. 16(1), pages 1-14, January.
    3. Behdad Arandian & Amin Iraji & Hossein Alaei & Suraparb Keawsawasvong & Moncef L. Nehdi, 2022. "White-Tailed Eagle Algorithm for Global Optimization and Low-Cost and Low-CO 2 Emission Design of Retaining Structures," Sustainability, MDPI, vol. 14(17), pages 1-28, August.
    4. Thira Jearsiripongkul & Suraparb Keawsawasvong & Chanachai Thongchom & Chayut Ngamkhanong, 2022. "Prediction of the Stability of Various Tunnel Shapes Based on Hoek–Brown Failure Criterion Using Artificial Neural Network (ANN)," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    5. Sayan Sirimontree & Thira Jearsiripongkul & Van Qui Lai & Alireza Eskandarinejad & Jintara Lawongkerd & Sorawit Seehavong & Chanachai Thongchom & Peem Nuaklong & Suraparb Keawsawasvong, 2022. "Prediction of Penetration Resistance of a Spherical Penetrometer in Clay Using Multivariate Adaptive Regression Splines Model," Sustainability, MDPI, vol. 14(6), pages 1-16, March.

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