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The Uplift Capacity Prediction for Regular and Enlarged Piles in Sandy Soils Using Artificial Neural Networks |
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| รหัสดีโอไอ | |
| Creator | Shaymaa T. Kadhim |
| Title | The Uplift Capacity Prediction for Regular and Enlarged Piles in Sandy Soils Using Artificial Neural Networks |
| Contributor | Mustafa M. Khattab, Ahmed S. Abdulrasool, Humam Hussein Mohammed Al-Ghabawi |
| Publisher | Southeast Asian Geotechnical Society |
| Publication Year | 2568 |
| Journal Title | Geotechnical Engineering Journal of SEAGS & AGSSEA |
| Journal Vol. | N/A |
| Journal No. | 2 |
| Page no. | 1-8 |
| Keyword | Piles, Uplift Capacity, Sand, Artificial Neural Networks |
| URL Website | https://seags.ait.asia/ |
| Website title | AGSSEA & SEAGS |
| ISSN | 0046-5828 |
| Abstract | The uplift capacity of piles is considered as a crucial aspect in practice for a geotechnical engineer. Nowadays, artificial machine learning techniques have emerged as a powerful tool in engineering for prediction and estimation with reasonable accuracy. This paper investigates the uplift capacity of two types of single piles: regular and enlarged piles installed in sand using an artificial neural network (ANN). Different activation functions were used, and the ANN results were compared with those of other algorithms. The results showed that one unified machine learning model has proven its efficiency in giving reasonable and accurate estimates of the uplift capacity of regular and enlarged piles. The ANN algorithm had the best results compared with other algorithms (Random forests, XGBoost, and Adaboost) with a coefficient of determination R2 equal to 0.970151 and 0.96924 for training and testing data, respectively, while other algorithms showed a sign of over-fitting. Finally, the ANN model was compared to well-known theoretical models and the ANN had better results. |
