Swarm-based analysis through social behavior of grey wolf optimization and genetic programming to predict friction capacity of driven piles

  • Hossein MoayediEmail author
  • Mohammed Abdullahi Mu’azu
  • Loke Kok FoongEmail author
Original Article


The advantage of new data mining-based solutions, and more recently, optimization algorithms (i.e., basically swarm-based solutions) have enhanced traditional models of engineering structural analysis. This paper investigates social behavior of Grey Wolf Optimization (GWO) in improving the neural assessment of friction capacity (fs) of concrete driven pile systems. Besides, the genetic programming (GP) algorithm was also proposed to have comparison with the proposed GWO prediction outputs. To achieve this goal, four fs influential factors of pile length (m), pile diameter (cm), effective vertical stress (Sv), and undrained shear strength (Su) are considered for preparing the required dataset. A swarm size-based sensitivity analysis is then carried out to use the best-fitted structures (i.e., more convergency in the final output) of each ensemble. The results of the best prediction network from both above-mentioned sensitivity analyses were compared. The results show that both GWO and GP models presented excellent performance. The findings of neural networks varied based on the number of neurons in a single hidden layer and of course the level of its complexity. Based on R2 and RMSE, values of (0.9537 and 9.372) and (0.8963 and 7.045) are determined, for the training and testing datasets of MLP-based solution, respectively. On the contrary, for the GP and GWO-MLP proposed predictive models, the R2 of (0.9783 and 0.982) and (0.913 and 0.892) were found for the training and testing datasets. This proves the better performance of GWO when combined with MLP in predicting engineering solutions comparing to conventional MLP or GP-based combinations.


Hybrid model Concrete driven piles Genetic programming Multilayer perceptron 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department for Management of Science and Technology DevelopmentTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.Faculty of Civil EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam
  3. 3.Civil Engineering Department, College of EngineeringUniversity of Hafr Al-BatinAl Jamiah DistrictKingdom of Saudi Arabia
  4. 4.Institute of Research and DevelopmentDuy Tan UniversityDa NangVietnam

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