Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/xmlui/handle/10054/12045
Title: Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data, Gujarat, India
Authors: RAMAKRISHNAN, D
SINGH, TN
PURWAR, N
BARDE, KS
GULATI, A
GUPTA, S
Keywords: Resistance
Issue Date: 2008
Publisher: SPRINGER
Citation: COMPUTATIONAL GEOSCIENCES, 12(4), 491-501
Abstract: This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.
URI: http://dx.doi.org/10.1007/s10596-008-9088-8
http://dspace.library.iitb.ac.in/xmlui/handle/10054/12045
http://hdl.handle.net/10054/12045
ISSN: 1420-0597
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