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Type: Journal article
Title: Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results
Author: Ranasinghe, R.
Jaksa, M.
Kuo, Y.
Pooya Nejad, F.
Citation: Journal of Rock Mechanics and Geotechnical Engineering, 2017; 9(2):340-349
Publisher: Elsevier BV
Issue Date: 2017
ISSN: 1674-7755
Statement of
R.A.T.M. Ranasinghe, M.B. Jaksa, Y.L. Kuo, F. Pooya Nejad
Abstract: 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.
Keywords: Rolling dynamic compaction (RDC); ground improvement; Artificial neural network (ANN); Dynamic cone penetration (DCP) test
Description: Available online 27 February 2017
Rights: © 2017 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( licenses/by-nc-nd/4.0/).
DOI: 10.1016/j.jrmge.2016.11.011
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Civil and Environmental Engineering publications

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