Modeling of the angle of shearing resistance of soils using soft computing systems


Kayadelen C., Gunaydin O., Fener M., Demir A., Ozvan A.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.36, ss.11814-11826, 2009 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 36 Konu: 9
  • Basım Tarihi: 2009
  • Doi Numarası: 10.1016/j.eswa.2009.04.008
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Sayfa Sayısı: ss.11814-11826

Özet

Precise determination of the effective angle of shearing resistance (phi') value is a major concern and an essential criterion in the design process of the geotechnical structures, such as foundations, embankments, roads, slopes, excavation and liner systems for the solid waste. The experimental determination of phi' is often very difficult, expensive and requires extreme cautions and labor. Therefore many statistical and numerical modeling techniques have been suggested for the phi' value. However they can only consider no more than one parameter, in a simplified manner and do not provide consistent accurate prediction of the phi' value. This study explores the potential of Genetic Expression Programming, Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) computing paradigm in the prediction of phi' value of soils. The data from consolidated-drained triaxial tests (CID) conducted in this study and the different project in Turkey and literature were used for training and testing of the models. Four basic physical properties of soils that cover the percentage of fine grained (FG), the percentage of coarse grained (CG), liquid limit (LL) and bulk density (BD) were presented to the models as input parameters. The performance of models was comprehensively evaluated some statistical criteria. The results revealed that GEP model is fairly promising approach for the prediction of angle of shearing resistance of soils. The statistical performance evaluations showed that the GEP model significantly outperforms the ANN and ANFIS models in the sense of training performances and prediction accuracies. (C) 2009 Elsevier Ltd. All rights reserved.