کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
254901 503336 2014 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns
ترجمه فارسی عنوان
ماشین های بردار پشتیبانی شده برای پیش بینی قدرت فشاری یکسانی از ستون های جوش داده شده
کلمات کلیدی
داده کاوی، ماشین آلات بردار پشتیبانی، تجزیه و تحلیل میزان حساسیت، نرم خاک، مخلوط سیمان خاکی، بهبود خاک، تزریق جت، استحکام فشاری یک طرفه
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی

Learning from data is a very attractive alternative to “manually” learning. Therefore, in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. This approach, supported on advanced statistics analysis, is usually known as Data Mining (DM) and has been applied successfully in different knowledge domains. In the present study, we show that DM can make a great contribution in solving complex problems in civil engineering, namely in the field of geotechnical engineering. Particularly, the high learning capabilities of Support Vector Machines (SVMs) algorithm, characterized by it flexibility and non-linear capabilities, were applied in the prediction of the Uniaxial Compressive Strength (UCS) of Jet Grouting (JG) samples directly extracted from JG columns, usually known as soilcrete. JG technology is a soft-soil improvement method worldwide applied, extremely versatile and economically attractive when compared with other methods. However, even after many years of experience still lacks of accurate methods for JG columns design. Accordingly, in the present paper a novel approach (based on SVM algorithm) for UCS prediction of soilcrete mixtures is proposed supported on 472 results collected from different geotechnical works. Furthermore, a global sensitivity analysis is applied in order to explain and extract understandable knowledge from the proposed model. Such analysis allows one to identify the key variables in UCS prediction and to measure its effect. Finally, a tentative step toward a development of UCS prediction based on laboratory studies is presented and discussed.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers and Geotechnics - Volume 55, January 2014, Pages 132–140
نویسندگان
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