MATEC Web Conf.
Volume 149, 20182nd International Congress on Materials & Structural Stability (CMSS-2017)
|Number of page(s)||4|
|Section||Session 2 : Structures & Stability|
|Published online||14 February 2018|
Neural networks and principle component analysis approaches to predict pile capacity in sand
University of Bounamaa Djilali, Department of Civil Engineering, Khemis Miliana, Algeria
2 University of Science and Technology, Department of Civil Engineering, Houari Boumediene, Algeria
3 University of Sherbrooke, Department of Civil Engineering, Sherbrooke, Canada
Determination of pile bearing capacity from the in-situ tests has developed considerably due to the significant development of their technology. The project presented in this paper is a combination of two approaches, artificial neural networks and main component analyses that allow the development of a neural network model that provides a more accurate prediction of axial load bearing capacity based on the SPT test data. The retropropagation multi-layer perceptron with Bayesian regularization (RB) was used in this model. This was established by the incorporation of about 260 data, obtained from the published literature, of experimental programs for large displacement driven piles. The PCA method is proposed for compression and suppression of the correlation between these data. This will improve the performance of generalization of the model.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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