Estimation of load-bearing capacity of bored piles using machine learning models

Pham  Binh Thai; Dam  Duc Nguyen; Quynh-Anh Bui Thi; Manh Duc Nguyen; Thanh  Tien Vu; Indra  Prakash

doi:10.15625/2615-9783/17177

Author affiliations

Authors

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

DOI:

https://doi.org/10.15625/2615-9783/17177

Keywords:

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

Abstract

The bearing capacity of bored piles is an essential parameter in the foundation design of a 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 the 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, the tensile strength of main longitudinal steel bar, compressive strength of concrete, average SPT index at the tip of the pile, and 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 a good potential in predicting correctly the bearing capacity of bored piles on training data (R>0.93) and on testing data (R>0.88) but the performance of the SVM model is the best (R=0.985 for training and R=0.958 for testing). Thus, the SVM model can be used to accurately predict the bearing capacity of bored piles for properly designing the civil engineering structure foundation.

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