Computational Geosciences

, Volume 12, Issue 4, pp 491–501 | Cite as

Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data, Gujarat, India

  • D. Ramakrishnan
  • T. N. Singh
  • N. Purwar
  • K. S. Barde
  • Akshay. Gulati
  • S. Gupta
Original paper


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.


Liquefaction susceptibility Neural network Bhuj 


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Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • D. Ramakrishnan
    • 1
  • T. N. Singh
    • 1
  • N. Purwar
    • 2
  • K. S. Barde
    • 2
  • Akshay. Gulati
    • 1
  • S. Gupta
    • 2
  1. 1.Department of Earth SciencesIndian Institute of TechnologyPowaiIndia
  2. 2.Institute of TechnologyBanaras Hindu UniversityVaranasiIndia

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