Estimation of ultimate bearing capacity of bored piles using machine learning models


  • Binh Thai Pham University of Transport and Technology, Hanoi, Vietnam
  • Dam Duc Nguyen University of Transport and Technology, Hanoi, Vietnam
  • Quynh-Anh Thi Bui University of Transport and Technology, Hanoi, Vietnam
  • Manh Duc Nguyen Department of Geotechnical Engineering, University of Transport and Communications, Hanoi, Vietnam
  • Thanh Tien Vu Department of Technology, Smart Contruction Group, Hanoi, Vietnam
  • Indra Prakash DDG (R) Geological Survey of India, Gandhinagar 382010, India



Bearing capacity, bored pile, machine learning, ANN, ANFIS, SVM


The ultimate bearing capacity of bored piles is an essential parameter in foundation design of structure. In the present study, three Machine Learning (ML) methods namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized to estimate bearing capacity of bored piles based on limited engineering parameters of pile and soil obtained from 75 test sites in Vietnam. These parameters include pile diameter, pile length, tensile strength of main longitudinal steel bar, compressive strength of concrete, average SPT index at the tip of the pile, average SPT index at the pile body. Validation of the methods was verified using standard statistical metrics namely Root Mean Square Error (RMSE) and Correlation coefficient (R). The results show that all the proposed models have good potential in predicting correctly bearing capacity of bored piles on training data (R>0.93) and on testing data (R>0.88) but performance of the SVM model is the best (R:0.985 (training) and R:0.958 (testing). Thus SVM model can be used for the accurate prediction of ultimate bearing capacity of bored piles for proper designing of the civil engineering structure foundation.


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How to Cite

Binh Thai, P. ., Duc Nguyen, D. ., Bui Thi, Q.-A., Duc Nguyen, M., Tien Vu, T. ., & Prakash, I. . (2022). Estimation of ultimate bearing capacity of bored piles using machine learning models. Vietnam Journal of Earth Sciences.




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