Indian Geotechnical Journal

, Volume 45, Issue 3, pp 349–359 | Cite as

Lateral Load Capacity of Piles in Clay Using Genetic Programming and Multivariate Adaptive Regression Spline

  • Pradyut Kumar Muduli
  • Manas Ranjan Das
  • Sarat Kumar Das
  • Swagatika Senapati
Technical Note
First Online:
Received:
Accepted:
  • 223 Downloads

Abstract

This study presents the development of predictive models of lateral load capacity of pile in clay using artificial intelligence techniques; genetic programming and multivariate adaptive regression spline. The developed models are compared with different empirical models, artificial neural network (ANN) and support vector machine (SVM) models in terms of different statistical criteria. A ranking system is presented to evaluate present models with respect to above models. Model equations are presented and are found to be more compact compared to ANN and SVM models. A sensitivity analysis is made to identify the important inputs contributing to the lateral load capacity of pile.

Keywords

Lateral loaded pile Clay Genetic programming Statistical method 
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Copyright information

© Indian Geotechnical Society 2014

Authors and Affiliations

  • Pradyut Kumar Muduli
    • 1
  • Manas Ranjan Das
    • 2
  • Sarat Kumar Das
    • 3
  • Swagatika Senapati
    • 4
  1. 1.Department of Civil EngineeringBOSECuttackIndia
  2. 2.Department of Civil Engineering, ITERSOA UniversityBhubaneswarIndia
  3. 3.Department of Civil EngineeringNational Institute of Technology, RourkelaRourkelaIndia
  4. 4.Department of Civil EngineeringIndian Institute of TechnologyMadrasIndia

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