Prediction of Field Hydraulic Conductivity of Clay Liners Using an Artificial Neural Network and Support Vector Machine

被引：59

作者：

Das, Sarat Kumar ^{[1
]}

Samui, Pijush ^{[2
]}

Sabat, Akshaya Kumar ^{[3
]}

机构：

[1] Natl Inst Technol, Dept Civil Engn, Rourkela 769008, India

[2] VIT Univ, Ctr Disaster Mitigat & Management, Vellore 632014, Tamil Nadu, India

[3] SOA Univ, ITER, Dept Civil Engn, Bhubaneswar 751030, Orissa, India

来源：

INTERNATIONAL JOURNAL OF GEOMECHANICS | 2012年 / 12卷 / 05期

关键词：

Clay liners; Hydraulic conductivity; Field measurements; Artificial neural networks; Support vector machine; KOZENY-CARMAN; SOILS; GOODBYE; HAZEN; HELLO;

D O I：

10.1061/(ASCE)GM.1943-5622.0000129

中图分类号：

P5 [地质学];

学科分类号：

0709 ; 081803 ;

摘要：

This paper describes the application of artificial neural network (ANN) and support vector machine (SVM) methods for prediction of field hydraulic conductivity of clay liners based on in situ test results such as compaction characteristics, lift thickness, number of lift, and soil classification tests like Atterberg's limits and grain size. Statistical performances criteria, root mean square error, correlation coefficient, coefficient of determination, and overfitting ratio are used to compare different ANN and SVM models. Different algorithms are discussed for identification of important soil parameters affecting the hydraulic conductivity of clay liners. A model equation based on the parameters obtained using SVM is also discussed. (C) 2012 American Society of Civil Engineers.

引用

页码：606 / 611

页数：6

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