کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4923795 1363067 2017 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Full Length ArticleApplication of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results
ترجمه فارسی عنوان
طول کامل مقاله استفاده از شبکه های عصبی مصنوعی برای پیش بینی تاثیر تراکم پویایی نورد با استفاده از آزمون پویایی سنج مخروطی پویا
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات مهندسی ژئوتکنیک و زمین شناسی مهندسی
چکیده انگلیسی


- Application of artificial neural networks (ANNs) for a priori prediction of the effectiveness of rolling dynamic compaction (RDC) is presented.
- The models are incorporated with in-situ dynamic cone penetration (DCP) test data.
- The resulting optimal ANN model demonstrates very good accuracy and a robust behavior when assessed in the parametric study.
- In order to facilitate its adoption in practice, the optimal ANN is disseminated as a series of simplified equations.

Rolling dynamic compaction (RDC), which involves the towing of a noncircular module, is now widespread and accepted among many other soil compaction methods. However, to date, there is no accurate method for reliable prediction of the densification of soil and the extent of ground improvement by means of RDC. This study presents the application of artificial neural networks (ANNs) for a priori prediction of the effectiveness of RDC. The models are trained with in situ dynamic cone penetration (DCP) test data obtained from previous civil projects associated with the 4-sided impact roller. The predictions from the ANN models are in good agreement with the measured field data, as indicated by the model correlation coefficient of approximately 0.8. It is concluded that the ANN models developed in this study can be successfully employed to provide more accurate prediction of the performance of the RDC on a range of soil types.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Rock Mechanics and Geotechnical Engineering - Volume 9, Issue 2, April 2017, Pages 340-349
نویسندگان
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