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Artificial intelligence-optimized design for dynamic compaction in granular soils
https://orcid.org/http://orcid.org/0000-0001-7033-4653
This study presents a novel procedure and mathematical model employing four artificial intelligence AI algorithms to predict the cumulative degree of soil compaction CDSC during dynamic compaction DC. The four AI algorithms used in this study involve artificial neural network ANN, support vector regression SVR, gradient boosting machine GBM, and random forest RF. Input variables involve the average SPT N value Nini before dynamic compaction, cumulative applied energy normalized with a cross-sectional area of tamper Ea, and the number of the tamper drops Ndrops. Apart from cross-validation with a testing set, additional in situ test data gathered from a different section within the study site are used to assess the generalization capacity of the AI models. In addition, out-of-distribution analyses for the four AI algorithms are conducted in the context of parametric studies. The CDSC prediction performance for the four AI models leads to high prediction metrics of accuracy with the r2 greater than 0.9 for the testing scenario while the r2 of the other AI models is greater than 0.9 when out-of-sample data are considered except for the GBM. The ANN appears to be the best model as the parametric study takes into account out-of-distribution data and suggests a robust relationship between input variables and CDSC that is more coherent with engineering principles for DC. Finally, the ANN model is utilized to develop a new mathematical model for CDSC prediction.
Artificial intelligence-optimized design for dynamic compaction in granular soils
https://orcid.org/http://orcid.org/0000-0001-7033-4653
This study presents a novel procedure and mathematical model employing four artificial intelligence AI algorithms to predict the cumulative degree of soil compaction CDSC during dynamic compaction DC. The four AI algorithms used in this study involve artificial neural network ANN, support vector regression SVR, gradient boosting machine GBM, and random forest RF. Input variables involve the average SPT N value Nini before dynamic compaction, cumulative applied energy normalized with a cross-sectional area of tamper Ea, and the number of the tamper drops Ndrops. Apart from cross-validation with a testing set, additional in situ test data gathered from a different section within the study site are used to assess the generalization capacity of the AI models. In addition, out-of-distribution analyses for the four AI algorithms are conducted in the context of parametric studies. The CDSC prediction performance for the four AI models leads to high prediction metrics of accuracy with the r2 greater than 0.9 for the testing scenario while the r2 of the other AI models is greater than 0.9 when out-of-sample data are considered except for the GBM. The ANN appears to be the best model as the parametric study takes into account out-of-distribution data and suggests a robust relationship between input variables and CDSC that is more coherent with engineering principles for DC. Finally, the ANN model is utilized to develop a new mathematical model for CDSC prediction.
Artificial intelligence-optimized design for dynamic compaction in granular soils
https://orcid.org/http://orcid.org/0000-0001-7033-4653
This study presents a novel procedure and mathematical model employing four artificial intelligence AI algorithms to predict the cumulative degree of soil compaction CDSC during dynamic compaction DC. The four AI algorithms used in this study involve artificial neural network ANN, support vector regression SVR, gradient boosting machine GBM, and random forest RF. Input variables involve the average SPT N value Nini before dynamic compaction, cumulative applied energy normalized with a cross-sectional area of tamper Ea, and the number of the tamper drops Ndrops. Apart from cross-validation with a testing set, additional in situ test data gathered from a different section within the study site are used to assess the generalization capacity of the AI models. In addition, out-of-distribution analyses for the four AI algorithms are conducted in the context of parametric studies. The CDSC prediction performance for the four AI models leads to high prediction metrics of accuracy with the r2 greater than 0.9 for the testing scenario while the r2 of the other AI models is greater than 0.9 when out-of-sample data are considered except for the GBM. The ANN appears to be the best model as the parametric study takes into account out-of-distribution data and suggests a robust relationship between input variables and CDSC that is more coherent with engineering principles for DC. Finally, the ANN model is utilized to develop a new mathematical model for CDSC prediction.
Artificial intelligence-optimized design for dynamic compaction in granular soils
Acta Geotech.
Ewusi-Wilson, Rodney (author) / Lee, Changho (author) / Park, Junghee (author)
Acta Geotechnica ; 19 ; 3487-3503
2024-06-01
17 pages
Article (Journal)
Electronic Resource
English
Artificial intelligence , Cumulative degree of soil compaction , Dynamic compaction , Machine learning , Standard penetration test Engineering , Geoengineering, Foundations, Hydraulics , Solid Mechanics , Geotechnical Engineering & Applied Earth Sciences , Soil Science & Conservation , Soft and Granular Matter, Complex Fluids and Microfluidics
Artificial intelligence-optimized design for dynamic compaction in granular soils
| Springer Verlag | 2024
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