Neural Computing and Applications

, Volume 29, Issue 11, pp 1115–1125 | Cite as

Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming

  • Danial Jahed Armaghani
  • Roohollah Shirani Faradonbeh
  • Hossein RezaeiEmail author
  • Ahmad Safuan A. Rashid
  • Hassan Bakhshandeh Amnieh
Original Article


The settlement design of bored piles socketed into rock has received considerable attention. Although many design methods of pile settlement are recommended in the literature, proposing new/practical technique(s) with higher performance prediction is of advantage. A new model based on gene expression programming (GEP) is presented in this paper for predicting the settlement of the rock-socketed pile. To do this, 96 piles socketed in different types of rock (mostly granite) as part of the Klang Valley Mass Rapid Transit project, Malaysia, were studied. In order to propose a predictive model with higher performance prediction, a series of GEP analyses were conducted using the most important factors on pile settlement, i.e. ratio of length in soil layer to length in rock layer, ratio of total length to diameter, uniaxial compressive strength, standard penetration test and ultimate bearing capacity. For comparison purpose, using the same dataset, linear multiple regression (LMR) technique was also performed. After developing the equations, their prediction performances were checked through several performance indices. The results demonstrated the feasibility of GEP-based predictive model of settlement. Coefficients of determination (CoD) values of 0.872 and 0.861 for training and testing datasets of GEP equation, respectively, show superiority of this model in predicting pile settlement while these values were obtained as 0.835 and 0.751 for the LMR model. Moreover, root mean square error (RMSE) values of (1.293 and 1.656 for training and testing) and (1.737 and 1.767 for training and testing) were achieved for the developed GEP and LMR models, respectively.


Rock-socketed pile Settlement Gene expression programming Linear multiple regression 



This research was supported by the Project Delivery Partner of the KVMRT Project, MMC-Gamuda KVMRT (PDP) Sdn Bhd, which provided the test data used in this paper. The authors would like to express their gratitude to the Head of Geotechnical, Ir. Andrew Yeow Pow Kwei and Raja Shahrom Nizam Shah bin Raja Shoib for support and encouragement  provided throughout this study.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.


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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Danial Jahed Armaghani
    • 1
  • Roohollah Shirani Faradonbeh
    • 2
  • Hossein Rezaei
    • 3
    Email author
  • Ahmad Safuan A. Rashid
    • 4
  • Hassan Bakhshandeh Amnieh
    • 5
  1. 1.Department of Civil and Environmental EngineeringAmirkabir University of TechnologyTehranIran
  2. 2.Department of Mining, Faculty of EngineeringTarbiat Modares UniversityTehranIran
  3. 3.Department of Civil Engineering, Faculty of EngineeringLorestan UniversityLorestanIran
  4. 4.Department of Geotechnics and Transportation, Faculty of Civil EngineeringUniversiti Teknologi MalaysiaSkudaiMalaysia
  5. 5.School of Mining, College of EngineeringUniversity of TehranTehranIran

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