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

被引：16

作者：

Muduli P.K. ^{[1
]}

Das M.R. ^{[2
]}

Das S.K. ^{[3
]}

Senapati S. ^{[4
]}

机构：

[1] Department of Civil Engineering, BOSE, Cuttack, 753007, Odisha

[2] Department of Civil Engineering, ITER, SOA University, Bhubaneswar, 751030, Odisha

[3] Department of Civil Engineering, National Institute of Technology, Rourkela, Rourkela, 769008, Odisha

[4] Department of Civil Engineering, Indian Institute of Technology, Madras, Tamilnadu

来源：

Indian Geotechnical Journal | 2015年 / 45卷 / 3期

关键词：

Clay; Genetic programming; Lateral loaded pile; Statistical method;

D O I：

10.1007/s40098-014-0142-2

中图分类号：

学科分类号：

摘要：

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. © 2014, Indian Geotechnical Society.

引用

页码：349 / 359

页数：10

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