Bayesian Neural Network Analysis of Undrained Side Resistance of Drilled Shafts
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 131, Issue 1
Abstract
A Bayesian neural network algorithm was used to model the relationship between the soil undrained shear strength, the effective overburden stress, and the undrained side resistance alpha factor for drilled shafts. The database comprised 127 field load tests. The Bayesian approach to learning in neural networks is a recent enhancement to improve the generalization ability of neural networks. Instead of just giving a single “optimum” prediction, the Bayesian approach provides information on the characteristic error of the prediction that arises from the uncertainty associated with interpolating noisy data. The developed neural network model provides good estimates of the undrained side resistance adhesion factor. Parametric studies using the trained neural network model suggest that the effective overburden stress directly or indirectly has an influence on the adhesion factor for drilled shafts. One distinct benefit of this neural network model is the computation of the error bars on the predictions of the adhesion factor. These error bars will aid in giving confidence to the predicted values and the interpretation of the results.
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Acknowledgments
This research was conducted while the first writer was on sabbatical leave at the School of Civil and Environmental Engineering, Cornell University. The financial support from the Nanyang Technological University is gratefully acknowledged.
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Information & Authors
Information
Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 131 • Issue 1 • January 2005
Pages: 84 - 93
Copyright
© 2004 ASCE.
History
Received: Dec 7, 2001
Accepted: Mar 25, 2004
Published online: Jan 1, 2005
Published in print: Jan 2005
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