Chapter
Apr 26, 2012
Axial Capacity Prediction for Driven Piles using ANN: Model Comparison
Authors: T. M. H. Lok and W. F. CheAuthor Affiliations
Publication: Geotechnical Engineering for Transportation Projects
Abstract
A comparison of three different models using back-propagation neural network for estimation of pile bearing capacity from dynamic stress wave data was made. The bearing capacity predicted by TNOWAVE was employed as the desired output in training. The study shows that the neural network models generally predict total bearing capacity more favorably if both the stress wave data and the properties of the driven pile are considered as the input parameters. In addition, better selection of input parameters rather than the increase number of input parameters will improve the accuracy of the prediction.
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Published In
Geotechnical Engineering for Transportation Projects
Pages: 697 - 704
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© 2004 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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Authors
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T. M. H. Lok
University of Macau, Macau S.A.R., China
W. F. Che
Civil Engineering Laboratory of Macau, Macau S.A.R., China
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ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.