Comparison of Artificial Neural Networks models with correlative works on undrained shear strength

Sivrikaya O.

EURASIAN SOIL SCIENCE, vol.42, no.13, pp.1487-1496, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 42 Issue: 13
  • Publication Date: 2009
  • Doi Number: 10.1134/s1064229309130092
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1487-1496
  • Karadeniz Technical University Affiliated: No


In recent years, the Artificial Neural Network (ANN) modelling that has been used in the solution of the complex problems has gained an increasing interest in soil science. The ANN modelling is also getting more popular in soil mechanics applications. It is a preferable method among the other approaching methods because of having quick results in test phase in short time. This paper describes the ANN models for estimating undrained shear strength (S-u) of cohesive soils from SPT (Standard Penetration Test) data with index properties in Turkey. The performance of the ANN models is investigated using different input variables such as measured N, corrected N (N-60) value, natural water content (w(n)), liquid limit (w(L)), plasticity index (I-p). In this study the ANN models are compared to empirical methods. The results indicate the superior performance of ANN models over the empirical methods.