Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods
Authors:
- G.R. KhanlariGKG.R. Khanlari
- M. HeidariMHM. Heidari
- A.A. MomeniAMA.A. Momeni
- Y. AbdilorYAY. Abdilor
- 1 March 2012
- journal article
- Published by Elsevier in Engineering Geology
- Vol. 131-132, 11-18
Abstract
No abstract availableKeywords
This publication has 22 references indexed in Scilit:
- Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programmingNeural Computing and Applications, 2008
- Prediction of residual friction angle of clays using artificial neural networkEngineering Geology, 2008
- Prediction of compressive and tensile strength of limestone via genetic programmingExpert Systems with Applications, 2008
- Reply to discussion by H. Sönmez and C. Gökceoglu on “A neural network approach for attenuation relationships: An application using strong ground motion data from Turkey” by H. Güllü and E. Erçelebi, Eng. Geol. 93 (2007) 65–81Engineering Geology, 2008
- A neural network approach for attenuation relationships: An application using strong ground motion data from TurkeyEngineering Geology, 2007
- A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoffJournal of Hydrology, 2007
- Application of back-propagation networks in debris flow predictionEngineering Geology, 2006
- Groundwater level forecasting using artificial neural networksJournal of Hydrology, 2005
- Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, VenezuelaEngineering Geology, 2005
- Modeling Time Dependent Swell of Clays Using Sequential Artificial Neural NetworksEnvironmental & Engineering Geoscience, 2003