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Axial capacity prediction for driven piles using ANN: Model comparison
Lok,T. M.H.1; Che,W. F.2
2004
Source PublicationGeotechnical Special Publication
Issue126 I
Pages697-704
AbstractA 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.
DOI10.1061/40744(154)56
URLView the original
Language英語English
Scopus ID2-s2.0-10944240790
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Citation statistics
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Geo-Institute,University of Macau,Macao
2.Civ. Engineering Laboratory of Macau,Macao
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Lok,T. M.H.,Che,W. F.. Axial capacity prediction for driven piles using ANN: Model comparison[C], 2004, 697-704.
APA Lok,T. M.H.., & Che,W. F. (2004). Axial capacity prediction for driven piles using ANN: Model comparison. Geotechnical Special Publication(126 I), 697-704.
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