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Handbook of Genetic Programming Applications pp 309–343Cite as

Evaluation of Liquefaction Potential of Soil Based on Shear Wave Velocity Using Multi-Gene Genetic Programming

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Abstract

In this chapter, liquefaction potential of soil is evaluated within deterministic as well as probabilistic framework based on the post-liquefaction shear wave velocity (V s) measurement data using a soft computing technique, multi-gene genetic programming (MGGP), which is a variant genetic programming (GP). On the basis of the developed limit state function by the MGGP, a mapping function is presented to correlate probability of liquefaction (P L) with factor of safety (F s) against liquefaction using Bayesian theory of conditional probability. Two examples are presented to compare the developed MGGP-based deterministic as well as probabilistic methods with those of available artificial neural network (ANN)-based methods. The findings from the above two examples confirm that MGGP-based methods are more accurate than the ANN-based methods in predicting the liquefied as well as non-liquefied cases.

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Authors and Affiliations

  1. Civil Engineering Department, National Institute of Technology Rourkela, Rourkela, 769008, India

    Pradyut Kumar Muduli & Sarat Kumar Das

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  1. Pradyut Kumar Muduli
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  2. Sarat Kumar Das
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Correspondence to Sarat Kumar Das .

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Editors and Affiliations

  1. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, USA

    Amir H. Gandomi

  2. Department of Civil & Environmental Engineering, Michigan State University, East Lansing, Michigan, USA

    Amir H. Alavi

  3. Department of Computer Science and Information Systems, University of Limerick, Limerick, Limerick, Ireland

    Conor Ryan

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Muduli, P.K., Das, S.K. (2015). Evaluation of Liquefaction Potential of Soil Based on Shear Wave Velocity Using Multi-Gene Genetic Programming. In: Gandomi, A., Alavi, A., Ryan, C. (eds) Handbook of Genetic Programming Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-20883-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-20883-1_12

  • Publisher Name: Springer, Cham

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