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

Sarat Kumar Das (), Ranajeet Mohanty (), Madhumita Mohanty () and Mahasakti Mahamaya ()
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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

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2020, vol. 103, issue 2, No 37, 2393 pages

Abstract: 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.

Keywords: ANN; Feature selection; In situ tests; Liquefaction; MARS; MOSOS; Multi-objective optimization; NSGA-II (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11069-020-04089-3

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