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

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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.

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Authors and Affiliations

  1. Department of Earth Sciences, Indian Institute of Technology, Powai, Mumbai, 400 076, India

    D. Ramakrishnan, T. N. Singh & Akshay. Gulati

  2. Institute of Technology, Banaras Hindu University, Varanasi, 221 005, India

    N. Purwar, K. S. Barde & S. Gupta

Authors
  1. D. Ramakrishnan
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  2. T. N. Singh
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  3. N. Purwar
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  4. K. S. Barde
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  5. Akshay. Gulati
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  6. S. Gupta
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Correspondence to T. N. Singh.

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Ramakrishnan, D., Singh, T.N., Purwar, N. et al. Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data, Gujarat, India. Comput Geosci 12, 491–501 (2008). https://doi.org/10.1007/s10596-008-9088-8

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  • DOI: https://doi.org/10.1007/s10596-008-9088-8

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