New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization

KALINLI A., Acar M. C., Gunduz Z.

ENGINEERING GEOLOGY, vol.117, pp.29-38, 2011 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 117
  • Publication Date: 2011
  • Doi Number: 10.1016/j.enggeo.2010.10.002
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.29-38
  • Keywords: Ultimate bearing capacity, Shallow foundations, Neural network, Ant colony optimization, PREDICTION, MODEL
  • Middle East Technical University Affiliated: No


In this study, two different approaches are proposed to determine the ultimate bearing capacity of shallow foundations on granular soil. Firstly, an artificial neural network (ANN) model is proposed to predict the ultimate bearing capacity. The performance of the proposed neural model is compared with results of the Adaptive Neuro Fuzzy Inference System, Fuzzy Inference System and ANN, which are taken in literature. It is clearly seen that the performance of the ANN model in our study is better than that of the other prediction methods. Secondly, an improved Meyerhof formula is proposed for the computation of the ultimate bearing capacity by using a parallel ant colony optimization algorithm. The results achieved from the proposed formula are compared with those obtained from the Meyerhof, Hansen and Vesic computation formulas. Simulation results showed that the improved Meyerhof formula gave more accurate results than the other theoretical computation formulas. In conclusion, the improved Meyerhof formula could be successfully used for calculating the ultimate bearing capacity of shallow foundations. Crown (C) 2010 Copyright Published by Elsevier B.V. All rights reserved.