Journal
INTERNATIONAL JOURNAL OF GEOTECHNICAL ENGINEERING
Volume 7, Issue 4, Pages 388-394Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1179/1938636213Z.00000000041
Keywords
Pile load capacity; Statistical performance criteria; Artificial neural network; Extreme learning machine; Bayesian regularization neural network; Differential evolution neural network
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This study presents the development of a predictive model for the lateral load capacity of pile in clay using an artificial intelligence technique, extreme learning machine ( ELM). Other artificial intelligence models like artificial neural networks (ANN) ( Bayesian regularization neural network (BRNN), differential evolution neural network (DENN)) are also developed to compare the ELM model with them and available empirical models in terms of different statistical criteria. A ranking system is presented to evaluate the present models for identifying the best'' model. Sensitivity analyses are made to identify important inputs contributing to the developed models.
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