Premium
Estimation of compaction parameters of fine‐grained soils in terms of compaction energy using artificial neural networks
Author(s) -
Sivrikaya Osman,
Soycan Taner Y.
Publication year - 2010
Publication title -
international journal for numerical and analytical methods in geomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.419
H-Index - 91
eISSN - 1096-9853
pISSN - 0363-9061
DOI - 10.1002/nag.981
Subject(s) - compaction , artificial neural network , consistency (knowledge bases) , backpropagation , water content , soil water , geotechnical engineering , soil compaction , environmental science , soil science , computer science , engineering , machine learning , artificial intelligence
The determination of the compaction parameters such as optimum water content ( w opt ) and maximum dry unit weight (γ dmax ) requires great efforts by applying the compaction testing procedure which is also time consuming and needs significant amount of work. Therefore, it seems more reasonable to use the indirect methods for estimating the compaction parameters. In recent years, the artificial neural network (ANN) modelling has gained an increasing interest and is also acquiring more popularity in geotechnical engineering applications. This study deals with the estimation of the compaction parameters for fine‐grained soils based on compaction energy using ANN with the feed‐forward back‐propagation algorithm. In this study, the data including the results of the consistency tests, standard and modified Proctor tests, are collected from the literature and used in the analyses. The optimum structure of a network is determined for each ANN models. The analyses showed that the ANN models give quite reliable estimations in comparison with regression methods, thus they can be used as a reliable tool for the prediction of w opt and γ dmax . Copyright © 2010 John Wiley & Sons, Ltd.