Estimation of total bearing capacity of Pretensioned Spun Concrete Piles using a hybrid machine learning model

Souvik Pal; Duc Dam Nguyen; Dung Quang Vu; Linh Mai Tran; Giap Nguyen Trong; Giao Van Loi

doi:10.15625/2615-9783/24026

Author affiliations

Authors

Souvik Pal Computer Science and Engineering, Sister Nivedita University, Kolkata, India
Duc Dam Nguyen Geotechnical and Artificial Intelligence research group, University of Transport Technology, Hanoi 100000, Vietnam
Dung Quang Vu Geotechnical and Artificial Intelligence research group, University of Transport Technology, Hanoi 100000, Vietnam
Linh Mai Tran Joint stock company for Civil engineering consultant and construction - COFEC, Hanoi 100000, Vietnam
Nguyen Trong Giap Geotechnical and Artificial Intelligence research group, University of Transport Technology, Hanoi 100000, Vietnam
Giao Van Loi Geotechnical and Artificial Intelligence research group, University of Transport Technology, Hanoi 100000, Vietnam

DOI:

https://doi.org/10.15625/2615-9783/24026

Keywords:

Pile driving analyzer, Pretensioned Spun Concrete Piles, bearing capacity, hybrid model, cascade generalization, Gaussian processes

Abstract

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.

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