ASSESSING SEISMIC SOIL LIQUEFACTION POTENTIAL USING MACHINE LEARNING APPROACH
نویسندگان
چکیده
The liquefaction vulnerability of soil is generally related to a few parameters which are ordinarily measured by laboratory tests on distributed and undistributed under distinctive test conditions. This study uses methods based standard penetration assess criteria appraise the for deposits Chalus City placed in high seismic area. To overcome deficiencies these experimental strategies an ANN-based model has been created utilizing Artificial Intelligence technique anticipate liquefaction. proposed function plasticity index, liquid limit, water content, some other geotechnical parameters. Reliability index (β) probability (PL) have also determined both superior understanding their accuracies strength. First-order second moment (FOSM) reliability analysis embraced present paper. observation drawn from illustrates reliable conventional expectation rate regression as compared strategy. A strong shown assessing vulnerability, field information preparatory prediction, would be extraordinary help within designing.
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ژورنال
عنوان ژورنال: Journal of Civil Engineering, Science and Technology
سال: 2023
ISSN: ['2462-1382']
DOI: https://doi.org/10.33736/jcest.4982.2023