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Multi-objective feature selection (MOFS) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods

Author

Listed:
  • Sarat Kumar Das

    (Indian Institute of Technology (Indian School of Mines))

  • Ranajeet Mohanty

    (National Institute of Technology Rourkela)

  • Madhumita Mohanty

    (Indian Institute of Technology (Indian School of Mines))

  • Mahasakti Mahamaya

    (National Institute of Technology Rourkela)

Abstract

The prediction of liquefaction susceptibility for highly unbalanced database with limited and important input parameters is a crucial issue. The proposed multi-objective feature selection algorithms (MOFS) were applied to highly unbalanced databases of in situ tests: standard penetration test (SPT), cone penetration test (CPT) and shear wave velocity (Vs) test. Two multi-objective algorithms, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective symbiotic organisms search algorithm (MOSOS), were coupled with learning algorithms, artificial neural network (ANN) and multivariate adaptive regression spline (MARS) separately to effectively select the optimal parameters and simultaneously minimize the error. The obtained optimal point has approximately equal accuracy in both liquefiable and non-liquefiable conditions for training and testing. The important inputs found for models based on SPT are: (N1)60, amax and Mw; CPT: qc1, amax and CSR and Vs: Vs1, CSR, amax and Mw. The CPT-based models were found to be the most efficient.

Suggested Citation

  • Sarat Kumar Das & Ranajeet Mohanty & Madhumita Mohanty & Mahasakti Mahamaya, 2020. "Multi-objective feature selection (MOFS) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(2), pages 2371-2393, September.
    • Handle: RePEc:spr:nathaz:v:103:y:2020:i:2:d:10.1007_s11069-020-04089-3
    DOI: 10.1007/s11069-020-04089-3
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    Cited by:

    1. Yong-gang Zhang & Junbo Qiu & Yan Zhang & Yongyao Wei, 2021. "The adoption of ELM to the prediction of soil liquefaction based on CPT," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 539-549, May.
    2. Wan Fang & Guo Haixiang & Li Jinling & Gu Mingyun & Pan Wenwen, 2021. "Multi-objective Emergency Scheduling for Geological Disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(2), pages 1323-1358, January.

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