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

Muduli, Pradyut Kumar / Das, Manas Ranjan / Das, Sarat Kumar / Senapati, Swagatika

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

Muduli, Pradyut Kumar / Das, Manas Ranjan / Das, Sarat Kumar / Senapati, Swagatika

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.

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

Muduli, Pradyut Kumar / Das, Manas Ranjan / Das, Sarat Kumar / Senapati, Swagatika

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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Title:

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

Contributors:

Muduli, Pradyut Kumar (author) / Das, Manas Ranjan (author) / Das, Sarat Kumar (author) / Senapati, Swagatika (author)

Published in:

Indian Geotechnical Journal ; 45 ; 349-359

Publication date:

2015-01-03

Size:

11 pages

ISSN:

2277-3347 , 0971-9555

DOI:

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

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

English

Keywords:

Lateral loaded pile , Clay , Genetic programming , Statistical method Engineering , Geoengineering, Foundations, Hydraulics

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