Prediction of compressibility parameters of the soils using artificial neural network-Reference-Cited by-同舟云学术

Prediction of compressibility parameters of the soils using artificial neural network

Published:2016-10-18 Issue:1 Volume:5 Page:
ISSN:2193-1801
Container-title:SpringerPlus
language:en
Short-container-title:SpringerPlus

Author:

Kurnaz T. Fikret,Dagdeviren Ugur,Yildiz Murat,Ozkan Ozhan

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://link.springer.com/content/pdf/10.1186/s40064-016-3494-5.pdf

Reference31 articles.

1. Akayuli CFA, Ofosu B (2013) Empirical model for estimating compression index from physical properties of weathered birimian phyllites. Electron J Geotech Eng 18:6135–6144

2. Azzouz AS, Krizek RJ, Corotis RB (1976) Regression analysis of soil compressibility. Soils Found 16(2):19–29

3. Bae W, Heo TY (2011) Prediction of compression index using regression analysis of transformed variables method. Mar Georesour Geotechnol 29(1):76–94

4. Chik Z, Aljanabi QA, Kasa A, Taha MR (2014) Tenfold cross validation artificial neural network modeling of the settlement behavior of a stone column under a highway embankment. Arab J Geosci 7(11):4877–4887

5. Demir A (2015) New computational network models for better predictions of the soil compression index. Acta Geotech Slov 12(1):59–69

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