Prediction Method of Vertical Ultimate Bearing Capacity of Single Pile Based on Support Vector Machine

Abstract:

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By comprehensively analyzing the main factors affecting vertical ultimate bearing capacity of single pile, a prediction model of Support Vector Machine (SVM), which discusses the nonlinear relationship between vertical ultimate bearing capacity of single pile and influencing factors and analyzes the parameters on the performance of the model through sample knowledge learning, is established in this paper. The research results indicate that, SVM model, which is compared with BP neural networks model, possesses simple structure, flexible adaptability, high precision and powerful generalization ability, and can accurately reflect the actual mechanical characteristics of pile, therefore, SVM is an effective method for predicting vertical ultimate bearing capacity of single pile.

Info:

Periodical:

Advanced Materials Research (Volumes 168-170)

Edited by:

Lijuan Li

Pages:

2278-2282

DOI:

10.4028/www.scientific.net/AMR.168-170.2278

Citation:

Y. J. Liu et al., "Prediction Method of Vertical Ultimate Bearing Capacity of Single Pile Based on Support Vector Machine", Advanced Materials Research, Vols. 168-170, pp. 2278-2282, 2011

Online since:

December 2010

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Price:

$35.00

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