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 dyn...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/7053228 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849307198200479744 |
|---|---|
| author | Rodney Ewusi-Wilson Junghee Park Boyoung Yoon Changho Lee |
| author_facet | Rodney Ewusi-Wilson Junghee Park Boyoung Yoon Changho Lee |
| author_sort | Rodney Ewusi-Wilson |
| collection | DOAJ |
| description | 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 (Nbefore), averaged SPT N value after dynamic compaction (Nafter), 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 (r2), and root mean square error (RMSE). The model with the least MAE and RMSE and the highest r2 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 r2 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. |
| format | Article |
| id | doaj-art-1be8d8022f7346d48dcfcb3a006eba2d |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-1be8d8022f7346d48dcfcb3a006eba2d2025-08-20T03:54:51ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/7053228Geostatistics and Artificial Intelligence Applications for Spatial Evaluation of Bearing Capacity after Dynamic CompactionRodney Ewusi-Wilson0Junghee Park1Boyoung Yoon2Changho Lee3Department of Architecture and Civil EngineeringSchool of CivilSchool of CivilDepartment of Civil EngineeringThis 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 (Nbefore), averaged SPT N value after dynamic compaction (Nafter), 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 (r2), and root mean square error (RMSE). The model with the least MAE and RMSE and the highest r2 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 r2 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.http://dx.doi.org/10.1155/2022/7053228 |
| spellingShingle | Rodney Ewusi-Wilson Junghee Park Boyoung Yoon Changho Lee Geostatistics and Artificial Intelligence Applications for Spatial Evaluation of Bearing Capacity after Dynamic Compaction Advances in Civil Engineering |
| title | Geostatistics and Artificial Intelligence Applications for Spatial Evaluation of Bearing Capacity after Dynamic Compaction |
| title_full | Geostatistics and Artificial Intelligence Applications for Spatial Evaluation of Bearing Capacity after Dynamic Compaction |
| title_fullStr | Geostatistics and Artificial Intelligence Applications for Spatial Evaluation of Bearing Capacity after Dynamic Compaction |
| title_full_unstemmed | Geostatistics and Artificial Intelligence Applications for Spatial Evaluation of Bearing Capacity after Dynamic Compaction |
| title_short | Geostatistics and Artificial Intelligence Applications for Spatial Evaluation of Bearing Capacity after Dynamic Compaction |
| title_sort | geostatistics and artificial intelligence applications for spatial evaluation of bearing capacity after dynamic compaction |
| url | http://dx.doi.org/10.1155/2022/7053228 |
| work_keys_str_mv | AT rodneyewusiwilson geostatisticsandartificialintelligenceapplicationsforspatialevaluationofbearingcapacityafterdynamiccompaction AT jungheepark geostatisticsandartificialintelligenceapplicationsforspatialevaluationofbearingcapacityafterdynamiccompaction AT boyoungyoon geostatisticsandartificialintelligenceapplicationsforspatialevaluationofbearingcapacityafterdynamiccompaction AT changholee geostatisticsandartificialintelligenceapplicationsforspatialevaluationofbearingcapacityafterdynamiccompaction |