Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results
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 mea...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2017-04-01
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| Series: | Journal of Rock Mechanics and Geotechnical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674775516300890 |
| _version_ | 1818566500097720320 |
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| author | R.A.T.M. Ranasinghe M.B. Jaksa Y.L. Kuo F. Pooya Nejad |
| author_facet | R.A.T.M. Ranasinghe M.B. Jaksa Y.L. Kuo F. Pooya Nejad |
| author_sort | R.A.T.M. Ranasinghe |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T01:54:28Z |
| format | Article |
| id | doaj.art-dd653aa1b5524371a6f98ddba86d307d |
| institution | Directory Open Access Journal |
| issn | 1674-7755 |
| language | English |
| last_indexed | 2024-12-14T01:54:28Z |
| publishDate | 2017-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Rock Mechanics and Geotechnical Engineering |
| spelling | doaj.art-dd653aa1b5524371a6f98ddba86d307d2022-12-21T23:21:16ZengElsevierJournal of Rock Mechanics and Geotechnical Engineering1674-77552017-04-019234034910.1016/j.jrmge.2016.11.011Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test resultsR.A.T.M. RanasingheM.B. JaksaY.L. KuoF. Pooya NejadRolling 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.http://www.sciencedirect.com/science/article/pii/S1674775516300890Rolling dynamic compaction (RDC)Ground improvementArtificial neural network (ANN)Dynamic cone penetration (DCP) test |
| spellingShingle | R.A.T.M. Ranasinghe M.B. Jaksa Y.L. Kuo F. Pooya Nejad Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results Journal of Rock Mechanics and Geotechnical Engineering Rolling dynamic compaction (RDC) Ground improvement Artificial neural network (ANN) Dynamic cone penetration (DCP) test |
| title | Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results |
| title_full | Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results |
| title_fullStr | Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results |
| title_full_unstemmed | Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results |
| title_short | Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results |
| title_sort | application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results |
| topic | Rolling dynamic compaction (RDC) Ground improvement Artificial neural network (ANN) Dynamic cone penetration (DCP) test |
| url | http://www.sciencedirect.com/science/article/pii/S1674775516300890 |
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