Estimation of total bearing capacity of Pretensioned Spun Concrete Piles using a hybrid machine learning model - Pal, Souvik; Dam Nguyen, Duc; Quang Vu, Dung; Mai Tran, Linh; Nguyen Trong, Giap; Van Loi, Giap (Vietnam Journal of Earth Sciences, 2025)
In this paper, the main objective is to predict total bearing capacity (TBC) of pretensioned spun concrete piles (PSCP) using Machine Learning (ML) methods namely Reduced Error Pruning Tree (REPT), Gaussian Process (GP), Artificial Neural Networks (ANN) and two novel hybrid models including: Cascade Generalization based Gaussian Processes (CG-GP) and Cascade Generalization based Artificial Neural Networks (CG-ANN) based on data from 95 PSCP piles installed at the Hoa Binh 5 wind power plant project in Vietnam. For model development, field-estimated TBC values obtained from Pile Driving Analyzer (PDA) tests were used as the output parameter. The predictive capability of the models was validated using common statistical indicators, namely Mean Absolute Error (MAE), Coefficient of Determination (R2) and Root Mean Square Error (RMSE) with 70% of the data used for training and 30% for testing. The results indicated that the proposed hybrid CG-ANN model (R² = 0.935, RMSE = 44.691 ton, MAE = 30.215 ton) outperformed all other models including CG-GP (R2 = 0.929, RMSE = 50.738 ton, MAE = 37.812 ton), Artificial Neural Networks - ANN (R2 = 0.926, RMSE = 47.963 ton, MAE = 32.167 ton), REPT (R2 = 0.776, RMSE = 75.350 ton, MAE = 53.115 ton) and GP (R2 = 0.916, RMSE = 52.785 ton, MAE = 39.967 ton) in the correct prediction of the TBC of PSCP. The results demonstrate that the hybrid CG-ANN model can serve as an efficient and reliable tool for rapid, accurate estimation of PSCP bearing capacity, thereby helping reduce the time and cost associated with elaborate field testing.