Geostatistics and Artificial Intelligence Applications for Spatial Evaluation of Bearing Capacity after Dynamic Compaction

This study employs geostatistical and artificial intelligence (AI) methods to estimate the degree of ground improvement after dynamic compaction. We implement artificial neural network (ANN) and random forest (RF) for artificial intelligence spatial interpolation to investigate the efficiency of dynamic compaction considering the spatial distribution of the geotechnical parameters. Data used in this study involve averaged SPT N value before dynamic compaction (N-before), averaged SPT N value after dynamic compaction (N-after), applied energy (AE), X- and Y-coordinates at each borehole location, and degree of ground improvement (DI). This study uses the data obtained from a total of 42 borehole logs with an average depth of 17 m and testing depth intervals of 1.5 m after dynamic compaction and 26 SPT-N log data before dynamic compaction. An optimal spatial interpolation tool selected in this study develops a bearing capacity map after dynamic compaction. The model performance is examined using the correlations between SPT-based and predicted bearing capacity in the context of mean absolute error (MAE), coefficient of determination (r(2)), and root mean square error (RMSE). The model with the least MAE and RMSE and the highest r(2) is selected as optimal. The optimal RF (RFVD) model has an RMSE of 15.83 while out of the two geostatistical models considered OK recorded the lower RMSE of 22.62. Results show that RF spatial interpolation techniques outperform traditional geostatistical methods. The artificial neural network model shows good compatibility with physical and intuitive processes pertinent to dynamic compaction. The ANN resulted in a prediction RMSE of 0.11 for DI and an r(2) of 0.97. This unique approach for evaluating the efficiency of dynamic compaction will be useful to geotechnical engineers when designing site improvement projects, especially dynamic compaction by employing easily obtainable field data for coarse-grained soils.

Automated summary

This study employs geostatistical and artificial intelligence methods to estimate ground improvement after dynamic compaction. Results show that random forest spatial interpolation techniques outperform traditional geostatistical methods, and the artificial neural network model is compatible with physical and intuitive processes related to dynamic compaction. This unique approach is useful for geotechnical engineers when designing site improvement projects, particularly dynamic compaction for coarse-grained soils.