Prediction of Laboratory Peak Shear Stress Along the Cohesive Soil-Geosynthetic Interface Using Artificial Neural Network

Prasenjit Debnath; Ashim Kanti Dey

首页> 外文期刊>Geotechnical and geological engineering >Prediction of Laboratory Peak Shear Stress Along the Cohesive Soil-Geosynthetic Interface Using Artificial Neural Network

【24h】

Prediction of Laboratory Peak Shear Stress Along the Cohesive Soil-Geosynthetic Interface Using Artificial Neural Network

机译：Prediction of Laboratory Peak Shear Stress Along the Cohesive Soil-Geosynthetic Interface Using Artificial Neural Network

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

In general, soil-geosynthetic interface behaviour is modeled by interface element which involves the assumption of stiffness values which are difficult to determine experimentally. Most of the geosynthetic-reinforced earth structures fail at the interface of the geosynthetic and the soil due to slip or plastic yielding of the reinforced soil. Hence, for a proper design of the soil-geosynthetic interface, an artificial neural network (ANN) model can be used as an alternative approach for the prediction of the soil-geosynthetic interface behavior. The present study uses an ANN model to predict the peak shear stress along the cohesive soil-geosynthetic interface. Three-layer feed-forward back-propagation neural networks with 4, 10 and 15 hidden nodes using three different learning algorithms are examined. Out of three learning algorithms, Bayesian regularization learning algorithm with four hidden nodes is used for its highest coefficient of determination (R~2 = 0.988) for the testing set and all of the predicted data falling within the 99 prediction interval. The prediction performance of the ANN model with Bayesian regularization learning algorithm with four hidden nodes is compared with the multi-variable regression analysis.Different sensitivity analyses to quantify the most importance input parameters are also discussed. A neural interpretation diagram to visualize the effect of input parameters on the output is presented. Finally, a predicted model equation is obtained based on the neural network parameters.

著录项

来源
《Geotechnical and geological engineering》 |2017年第1期|445-461|共17页
作者
Prasenjit Debnath; Ashim Kanti Dey;
展开▼
作者单位

Department of Civil Engineering, NIT Silchar, Silchar 788010, India;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类地质学;
关键词
Artificial neural network; Peak shear stress; Sensitivity analysis; Multi-variable regression; Statistical analysis;

Prediction of Laboratory Peak Shear Stress Along the Cohesive Soil-Geosynthetic Interface Using Artificial Neural Network

摘要

著录项

相关主题

期刊订阅