کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
815774 906419 2014 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Vector machine techniques for modeling of seismic liquefaction data
ترجمه فارسی عنوان
تکنیک های ماشین بردار برای مدل سازی داده های لرزه ای مایع سازی
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی (عمومی)
چکیده انگلیسی
This article employs three soft computing techniques, Support Vector Machine (SVM); Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM), for prediction of liquefaction susceptibility of soil. SVM and LSSVM are based on the structural risk minimization (SRM) principle which seeks to minimize an upper bound of the generalization error consisting of the sum of the training error and a confidence interval. RVM is a sparse Bayesian kernel machine. SVM, LSSVM and RVM have been used as classification tools. The developed SVM, LSSVM and RVM give equations for prediction of liquefaction susceptibility of soil. A comparative study has been carried out between the developed SVM, LSSVM and RVM models. The results from this article indicate that the developed SVM gives the best performance for prediction of liquefaction susceptibility of soil.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ain Shams Engineering Journal - Volume 5, Issue 2, June 2014, Pages 355-360
نویسندگان
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