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| Titel |
Machine learning modelling for predicting soil liquefaction susceptibility |
| VerfasserIn |
P. Samui, T. G. Sitharam |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 11, no. 1 ; Nr. 11, no. 1 (2011-01-03), S.1-9 |
| Datensatznummer |
250009031
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| Publikation (Nr.) |
 copernicus.org/nhess-11-1-2011.pdf |
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| Zusammenfassung |
| This study describes two machine learning techniques applied to predict
liquefaction susceptibility of soil based on the standard penetration test
(SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine
learning technique which uses Artificial Neural Network (ANN) based on
multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt
backpropagation algorithm. The second machine learning technique uses the
Support Vector machine (SVM) that is firmly based on the theory of
statistical learning theory, uses classification technique. ANN and SVM have
been developed to predict liquefaction susceptibility using corrected SPT
[(N1)60] and cyclic stress ratio (CSR). Further, an attempt has been
made to simplify the models, requiring only the two parameters [(N1)60
and peck ground acceleration (amax/g)], for the prediction of
liquefaction susceptibility. The developed ANN and SVM models have also been
applied to different case histories available globally. The paper also
highlights the capability of the SVM over the ANN models. |
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