Ocean Systems Engineering

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Prediction of uplift capacity of suction caisson in clay using extreme learning machine

  • Muduli, Pradyut Kumar (Department of Civil Engineering, National Institute of Technology) ;
  • Das, Sarat Kumar (Department of Civil Engineering, National Institute of Technology) ;
  • Samui, Pijush (Centre for Disaster Mitigation and Management, VIT University) ;
  • Sahoo, Rupashree (Department of Civil Engineering, National Institute of Technology)
  • Received : 2013.06.29
  • Accepted : 2015.03.05
  • Published : 2015.03.25
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Abstract

This study presents the development of predictive models for uplift capacity of suction caisson in clay using an artificial intelligence technique, extreme learning machine (ELM). Other artificial intelligence models like artificial neural network (ANN), support vector machine (SVM), relevance vector machine (RVM) models are also developed to compare the ELM model with above models and available numerical models in terms of different statistical criteria. A ranking system is presented to evaluate present models in identifying the 'best' model. Sensitivity analyses are made to identify important inputs contributing to the developed models.

Keywords

References

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