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Prediction of Liquefaction Susceptibility of Clean Sandy Soils Using Artificial Intelligence Techniques

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Abstract

The liquefaction susceptibility of sandy soil is generally characterised by some parameters in the static liquefaction potential evaluation. These parameters are usually measured by static laboratory tests on distributed and undistributed samples under different test conditions. This study performs the ANN and genetic programming to estimate the static liquefaction susceptibility of clean sand soils based on experimental results to predict and develop an equation for the ratio of qmin/qpeak which is considered as the static liquefaction criterion. The qmin/qpeak model is a function of the minimum and maximum void ratios, relative density, initial effective confining pressure, and some other parameters. The findings of this study demonstrated that a good agreement between ANN and symbolic regression in predicting the ratio of qmin/qpeak based on laboratory tests. The possible application of the proposed qmin/qpeak equation is restricted by some limitations. The outcomes of the present work can be used in the preliminary liquefaction assessment of clean sandy soils prior to the complementary experimental studies.

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Abbreviations

AI :

Artificial intelligent

ANN :

Artificial neural network

B :

Skempton’s coefficient

C u :

Uniformity coefficient

CPT :

Cone Penetration Test

D r :

Relative density

D 50 :

Mean grains size

e max :

Maximum void ratio

e min :

Minimum void ratio

e :

Void ratio

GP :

Genetic programming

MGGP :

Multi-Gene Genetic Programming

I :

Number of input variables

q min :

Minimum deviatoric stress

q peak :

Initial peak deviatoric stress

RMSE :

Root mean square error

R 2 :

Coefficient of determination

SPT :

Standard Penetration Test

α :

The ratio of initial shear stresses to the initial effective confining pressure

σ′ 3c :

Initial effective confining pressure

σ 1 :

Axial stress

σ 3 c :

Confining pressure

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Acknowledgements

The first author sincerely acknowledges the funding received from the Higher Committee for Education Development in the Republic of Iraq in the form of a scholarship for his PhD study.

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Authors and Affiliations

  1. Department of Civil Engineering, Curtin University, Perth, Australia

    Ayad Salih Sabbar, Amin Chegenizadeh & Hamid Nikraz

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  1. Ayad Salih Sabbar
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  2. Amin Chegenizadeh
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Correspondence to Amin Chegenizadeh.

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Sabbar, A.S., Chegenizadeh, A. & Nikraz, H. Prediction of Liquefaction Susceptibility of Clean Sandy Soils Using Artificial Intelligence Techniques. Indian Geotech J 49, 58–69 (2019). https://doi.org/10.1007/s40098-017-0288-9

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  • DOI: https://doi.org/10.1007/s40098-017-0288-9

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