Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of Liquefaction Susceptibility of Clean Sandy Soils Using Artificial Intelligence Techniques
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 q min /q peak which is considered as the static liquefaction criterion. The q min /q peak 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 q min /q peak based on laboratory tests. The possible application of the proposed q min /q peak 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.
Prediction of Liquefaction Susceptibility of Clean Sandy Soils Using Artificial Intelligence Techniques
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 q min /q peak which is considered as the static liquefaction criterion. The q min /q peak 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 q min /q peak based on laboratory tests. The possible application of the proposed q min /q peak 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.
Prediction of Liquefaction Susceptibility of Clean Sandy Soils Using Artificial Intelligence Techniques
Sabbar, Ayad Salih (Autor:in) / Chegenizadeh, Amin (Autor:in) / Nikraz, Hamid (Autor:in)
Indian Geotechnical Journal ; 49 ; 58-69
09.11.2017
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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