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Estimation of Pile Bearing Capacity of Single Driven Pile in Sandy Soil Using Finite Element and Artificial Neural Network Methods

Author

Listed:
  • HARNEDI MAIZIR

    (Civil Engineering Department - Sekolah Tinggi Teknologi, Pekanbaru, Indonesia)

  • RENI SURYANITA

    (Civil Engineering Department, University of Riau, Pekanbaru, Indonesia)

  • HENDRA JINGGA

    (Civil Engineering Department, University of Riau, Pekanbaru, Indonesia)

Abstract

The good estimation of pile bearing capacity, which is derived by total axial pile bearing capacity can be obtained through numerous methods such as empirical, analytical and field test. Thus, application of the methods has been a difficult task due to the uncertainties of various factors related to properties of soil and rock which, unlike other engineering materials, are subject to spatial uncertainty. On the other hand, performing field tests such as static and dynamic load test is time consuming and expensive, hence the use of finite element and Artificial Neural Networks (ANNs) methods is often of interest. This paper explains the finite element and ANNs methods to estimate the pile bearing capacity of sandy soil. The ANNs method is used to estimate the bearing capacity by using dynamic load test data. The outputs of finite element modelling were compared with a well-established empirical method for estimation of the ultimate axial bearing capacity of the pile. The results show that finite element and ANNs prediction on the percentage of the ultimate load are close to each other.

Suggested Citation

  • Harnedi Maizir & Reni Suryanita & Hendra Jingga, 2016. "Estimation of Pile Bearing Capacity of Single Driven Pile in Sandy Soil Using Finite Element and Artificial Neural Network Methods," International Journal of Applied and Physical Sciences, Dr K.Vivehananthan, vol. 2(2), pages 45-50.
    • Handle: RePEc:apa:ijapss:2016:p:45-50
    DOI: 10.20469/ijaps.2.50003-2.pdf
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    Cited by:

    1. Mohamed Seghire Othman Djediden & Hicham Reguieg & Zoulikha Mekkakia Maaza, 2019. "A distributed intrusion detection system based on apache spark and scikit-learn library," Journal of Applied and Physical Sciences, Prof. Vakhrushev Alexander, vol. 5(1), pages 30-36.
    2. Latief Mahir Rachman, 2018. "Technical development to assess soil health using soil health index in Indonesia," Journal of Applied and Physical Sciences, Prof. Vakhrushev Alexander, vol. 4(3), pages 79-85.
    3. Savkovic B. & Kovac P. & Mankova I. & Gostimirovic M. & Rokosz K. & Rodic D., 2017. "Surface roughness modeling of semi solid aluminum milling by fuzzy logic," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 3(2), pages 34-46.
    4. Panova Elena & Oleynikova Galina, 2018. "Information Resources of Soil Nanoparticles Chemistry," International Journal of Applied and Physical Sciences, Dr K.Vivehananthan, vol. 4(2), pages 45-49.
    5. Reni Suryanita & Harnedi Maizir & Hendra Jingga, 2017. "Prediction of Structural Response Based on Ground Acceleration Using Artificial Neural Networks," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 3(2), pages 74-83.
    6. G. Turkoglu Demirkol & M. S. Ozcoban & N. Tufekci, 2017. "Removal rate of compacted clay soil in the batch and continuous reactors and its permeability," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 3(5), pages 176-183.
    7. Mohd. Ahmed & Saeed AlQadhi & Javed Mallick & Nabil Ben Kahla & Hoang Anh Le & Chander Kumar Singh & Hoang Thi Hang, 2022. "Artificial Neural Networks for Sustainable Development of the Construction Industry," Sustainability, MDPI, vol. 14(22), pages 1-21, November.

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