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

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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.

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Authors and Affiliations

  1. Department of Civil Engineering, BOSE, Cuttack, 753007, Odisha, India

    Pradyut Kumar Muduli

  2. Department of Civil Engineering, ITER, SOA University, Bhubaneswar, 751030, Odisha, India

    Manas Ranjan Das

  3. Department of Civil Engineering, National Institute of Technology, Rourkela, Rourkela, 769008, Odisha, India

    Sarat Kumar Das

  4. Department of Civil Engineering, Indian Institute of Technology, Madras, Tamilnadu, India

    Swagatika Senapati

Authors
  1. Pradyut Kumar Muduli
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  2. Manas Ranjan Das
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  3. Sarat Kumar Das
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  4. Swagatika Senapati
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Correspondence to Pradyut Kumar Muduli.

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Muduli, P.K., Das, M.R., Das, S.K. et al. Lateral Load Capacity of Piles in Clay Using Genetic Programming and Multivariate Adaptive Regression Spline. Indian Geotech J 45, 349–359 (2015). https://doi.org/10.1007/s40098-014-0142-2

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  • DOI: https://doi.org/10.1007/s40098-014-0142-2

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