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Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time

Author

Listed:
  • Mohammadreza Khanmohammadi

    (Department of Civil Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran)

  • Danial Jahed Armaghani

    (School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia)

  • Mohanad Muayad Sabri Sabri

    (Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

Abstract

Prediction of pile bearing capacity has been considered an unsolved problem for years. This study presents a practical solution for the preparation and maximization of pile bearing capacity, considering the effects of time after the end of pile driving. The prediction phase proposes an intelligent equation using a genetic programming (GP) model. Thus, pile geometry, soil properties, initial pile capacity, and time after the end of driving were considered predictors to predict pile bearing capacity. The developed GP equation provided an acceptable level of accuracy in estimating pile bearing capacity. In the optimization phase, the developed GP equation was used as input in two powerful optimization algorithms, namely, the artificial bee colony (ABC) and the grey wolf optimization (GWO), in order to obtain the highest bearing capacity of the pile, which corresponds to the optimum values for input parameters. Among these two algorithms, GWO obtained a higher value for pile capacity compared to the ABC algorithm. The introduced models and their modeling procedure in this study can be used to predict the ultimate capacity of piles in such projects.

Suggested Citation

  • Mohammadreza Khanmohammadi & Danial Jahed Armaghani & Mohanad Muayad Sabri Sabri, 2022. "Prediction and Optimization of Pile Bearing Capacity Considering Effects of Time," Mathematics, MDPI, vol. 10(19), pages 1-15, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3563-:d:929443
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    References listed on IDEAS

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    1. Diyuan Li & Zida Liu & Danial Jahed Armaghani & Peng Xiao & Jian Zhou, 2022. "Novel Ensemble Tree Solution for Rockburst Prediction Using Deep Forest," Mathematics, MDPI, vol. 10(5), pages 1-23, March.
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    Cited by:

    1. Danial Jahed Armaghani & Biao He & Edy Tonnizam Mohamad & Y.X Zhang & Sai Hin Lai & Fei Ye, 2022. "Applications of Two Neuro-Based Metaheuristic Techniques in Evaluating Ground Vibration Resulting from Tunnel Blasting," Mathematics, MDPI, vol. 11(1), pages 1-17, December.

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