کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6710832 | 503321 | 2015 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using Artificial Neural Network
ترجمه فارسی عنوان
پیش بینی مقاومت فشاری غیرقابل نفوذ خاک رس خاک پایدار ژئوپلیمر با استفاده از شبکه عصبی مصنوعی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Viability of Artificial Neural Network (ANN) in predicting unconfined compressive strength (UCS) of geopolymer stabilized clayey soil has been investigated in this paper. Factors affecting UCS of geopolymer stabilized clayey soil have also been reported. Ground granulated blast furnace slag (GGBS), fly ash (FA) and blend of GGBS and FA (GGBSÂ +Â FA) were chosen as source materials for geo-polymerization. 28Â day UCS of 283 stabilized samples were generated with different combinations of the experimental variables. Based on experimental results ANN based UCS predictive model was devised. The prediction performance of ANN model was compared to that of multi-variable regression (MVR) analysis. Sensitivity analysis employing different methods to quantify the importance of different input parameters were discussed. Finally neural interpretation diagram (NID) to visualize the effect of input parameters on UCS is also presented.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers and Geotechnics - Volume 69, September 2015, Pages 291-300
Journal: Computers and Geotechnics - Volume 69, September 2015, Pages 291-300
نویسندگان
Ruhul Amin Mozumder, Aminul Islam Laskar,