By Topic

Bearing Capacity Modeling of Composite Pile Foundation Using Parameter-Optimized RBF Neural Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Maosen Cao ; Coll. of Hydraulic & Civil Eng., Shandong Agric. Univ., Tai''an ; Baosheng Su

Radial basis function-artificial neural networks (RBF-ANNs) are used for bearing capacity modeling of composite foundation reinforced with deep mixing piles. Although RBF-ANNs possess significant advantages in terms of strong generalization, flexible adaptability to multi-independent variables and sufficient avoidance of local minima, their performance may be directly affected by two uncertain parameters, the width of radial basis kernel function (spread) and the goal error of training (err_goal). Up to now still no mature methods to determine the optimal parameter values. As an exploration, a novel method is proposed to determine the optimal parameter values by thoroughly searching over the possible interval of uncertain parameters. Moreover, a technique of reconstructing more samples from few original samples is put forward to improve the prediction precision of the RBF-ANNs. The proposed techniques are applied to the bearing capacity modeling of composite foundation reinforced with deep mixing piles. The results demonstrate that the uncertain parameter optimization and sample reconstruction techniques are capable of significantly improving the performance of RBF-ANNs

Published in:

Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on  (Volume:1 )

Date of Conference:

16-18 Oct. 2006