IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i6p3222-d767682.html
   My bibliography  Save this article

Prediction of Penetration Resistance of a Spherical Penetrometer in Clay Using Multivariate Adaptive Regression Splines Model

Author

Listed:
  • Sayan Sirimontree

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

  • Thira Jearsiripongkul

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

  • Van Qui Lai

    (Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City 700000, Vietnam
    Faculty of Civil Engineering, Vietnam National University Ho Chi Minh City (VNUHCM), Ho Chi Minh City 700000, Vietnam)

  • Alireza Eskandarinejad

    (Department of Civil Engineering, Faculty of Engineering, Golestan University, Gorgan P.O. Box 155, Iran)

  • Jintara Lawongkerd

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

  • Sorawit Seehavong

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

  • Chanachai Thongchom

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

  • Peem Nuaklong

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

  • Suraparb Keawsawasvong

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

Abstract

This paper presents the technique for solving the penetration resistance factor of a spherical penetrometer in clay under axisymmetric conditions by taking the adhesion factor, the embedded ratio, the normalized unit weight, and the undrained shear strength into account. The finite element limit analysis (FELA) is used to provide the upper bound (UB) or lower bound (LB) solutions, then the multivariate adaptive regression splines (MARS) model is used to train the optimal data between input and output database. The accuracy of MARS equations is confirmed by comparison with the finite element method and the validity of the present solutions was established through comparison to existing results. All numerical results of the penetration resistance factor have significance with three main parameters (i.e., the adhesion factor, the embedded ratio, the normalized unit weight, and the undrained shear strength). The failure mechanisms of spherical penetrometers in clay are also investigated, the contour profiles that occur around the spherical penetrometers also depend on the three parameters. In addition, the proposed technique can be used to estimate the problems that are related or more complicated in soft offshore soils.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3222-:d:767682
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/6/3222/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/6/3222/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel Barry, 1993. "7. Applied Nonparametric Regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 156(1), pages 128-129, January.
    2. Wiktor Halecki & Tomasz Kowalik & Andrzej Bogdał, 2019. "Multiannual Assessment of the Risk of Surface Water Erosion and Metal Accumulation Indices in the Flysch Stream Using the MARS Model in the Polish Outer Western Carpathians," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Thira Jearsiripongkul & Van Qui Lai & Suraparb Keawsawasvong & Thanh Son Nguyen & Chung Nguyen Van & Chanachai Thongchom & Peem Nuaklong, 2022. "Prediction of Uplift Capacity of Cylindrical Caissons in Anisotropic and Inhomogeneous Clays Using Multivariate Adaptive Regression Splines," Sustainability, MDPI, vol. 14(8), pages 1-21, April.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3222-:d:767682. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.