現地試驗(SPT及CPT)評估土層剪力波速-基因演算法最佳化類神經網路
Date Issued
2003
Date
2003
Author(s)
左天雄
DOI
912211E002050
Abstract
Using optimizing artificial neural networks
(ANN) with genetic algorithms (GAs), the shear
wave velocity of soil stratum may be accessed by the
parameters obtained from standard penetration tests
(SPT) and electronic cone penetration tests (CPT)
respectively. The number of hidden layers, neurons,
epochs of ANN and the data applying to that are
obtained by GAs.To verify the methodology, shear
wave velocities evaluated by ANN may be
confirmed by compared with the results of in-situ
seismic tests, like SCPT or Cross-hole. In this study,
shear wave velocities predicted by ANN correspond
to the results of in-situ seismic tests and are more
reliable than those obtained by traditional regression
method.
Subjects
shear wave velocity
genetic algorithms(Gas)
in-site penetration test
and backpropagation neural
networks (BPNN)
networks (BPNN)
Publisher
臺北市：國立臺灣大學土木工程學系暨研究所
Type
report
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