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Ocean Systems Engineering
  Volume 5, Number 1, March 2015 , pages 41-54
DOI: https://doi.org/10.12989/ose.2015.5.1.041
 

Prediction of uplift capacity of suction caisson in clay using extreme learning machine
Pradyut Kumar Muduli, Sarat Kumar Das, Pijush Samui and Rupashree Sahoo

 
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 model 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.
 
Key Words
    suction caisson; uplift capacity; extreme learning machine; support vector machine; artificial neural network; statistical performance criteria
 
Address
Pradyut Kumar Muduli, Sarat Kumar Dasand Rupashree Sahoo: Department of Civil Engineering, National Institute of Technology, Rourkela, Odisha, India
Pijush Samui: Centre for Disaster Mitigation and Management, VIT University, Vellore-632014, India
 

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