Full Length ArticleApplication of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results

- 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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