State-of-the-art XGBoost, RF and DNN based soft-computing models for PGPN piles

ABSTRACT

Machine learning (ML) has made significant advancements in predictive modelling across many engineering sectors. However, predicting the bearing capacity of pre-bored grouted planted nodular (PGPN) piles remains a relatively unexplored area due to the complexity of the load-bearing mechanism, pile-soil interactions, and multiple variables involved. The study utilises state-of-the-art ML techniques such as extreme gradient boosting (XGBoost), random forest (RF), gradient boosting machines (GBMs), and deep learning-based simulation models. The dataset fed into the model comprises 81 case histories of static pile load tests conducted in various regions of Vietnam. The data was validated using descriptive statistics, sensitivity analysis, correlation matrix displays, SHAP plot analysis, and regression curves, with predictive performance validated through k-fold cross-validation. Among all the models tested, XGBoost (R² = 0.91, RMSE = 0.09) and RF (R² = 0.82, RMSE = 0.09) performed the best, while the deep neural network also yielded satisfactory results. However, GBM was found not to be a robust model for this analysis. The performance of the models was visually analysed using Violin plot comparisons and Taylor diagrams. The outcome of this study facilitates the safe and economical designs of the eco-friendly pile.

KEYWORDS:

• PGPN Pile
• Machine Learning
• XGBoost
• DNN
• RF

Acknowledgments

The authors are thankful to Prof. Tan Nguyen, Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, VietNam, for helping us with the dataset for the present study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data will be made available on reasonable request.