Abstract:
Pile foundations support structures by transferring loads to deep sub-surface strata, designed to
bear the maximum design load without failure. Recent studies are focused on developing
innovative models to estimate the pile capacity on efficient ground in less time. The pile load tests
are difficult to perform and time-consuming. So, this study aims to address existing gaps in
geotechnical engineering research, specifically in pile strength estimation, by deploying advanced
machine learning algorithms, namely Random Forest (RF), Support Vector Regression (SVR), and
Xtreme Gradient Boost (XG Boost), which are meticulously fine-tuned using hyperparameter
optimization techniques, such as Grid Search (GS) and Random Search (RS). The models were
formulated in a high-level programming language, namely Python. The model's efficacy was
assessed through Root Mean Square Error (RMSE), Coefficient of Determination (R2), and
Standard Deviation (SD). The test results show that each model performs well; however, the
XGBoost algorithm shows higher efficacy, with high accuracy on the data sets (R2 = 0.933).
