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.
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
PDF Viewer
Preview uses the same access rules as Full Text PDF (subscription, purchase, or open access).
