期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 26, 期 10, 页码 2305-2314出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2013.04.007
关键词
Neural network; Product-unit neural network; Hybrid training; Piled foundation
类别
资金
- Petroleum Technology Development Fund, Nigeria
- National Nature Science Foundation of China [41172252]
In general, neural network training is a nonlinear multivariate optimisation problem. Unlike previous studies, in the present study, particle swarm optimisation (PSO) and back-propagation (BP) algorithms were coupled to develop a robust hybrid training algorithm with both local and global search capabilities. To demonstrate the capacity of the proposed model, we applied the model to the predictions of the load-deformation behaviour of axially loaded piles. This is a soil-structure interaction problem, involving a complex mechanism of load transfer from the pile to the supporting geologic medium. A database of full scale pile loading tests is used to train and validate the product-unit network. The results show that the proposed hybrid learning algorithm simulates the load-deformation curve of axially loaded piles more accurately than other BP, PSO, and existing PSO-BP hybrid methods. The network developed using the proposed algorithm also turns out to be more accurate than hyperbolic and t-z models. (C) 2013 Elsevier Ltd. All rights reserved.
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