Assessment of pile drivability using random forest regression and multivariate adaptive regression splines

ABSTRACT

Driven pile is widely used as an effective and convenient structural component to transfer superstructure loads to deep stiffer soils. Nevertheless, during the design process of piles, due to the intrinsic complexity as well as various design variables, the internal stress state related to pile drivability remains unclear, which makes the analysis imprecise. Thus, the development of an accurate predictive model becomes emergent. This paper presents a practical approach to assess pile drivability in relation to the prediction of Maximum compressive stresses and Blow per foot using a series of machine learning algorithms. A database of more than 4000 piles is employed to construct random forest regression (RFR) and multivariate adaptive regression splines (MARS) models. The 10-fold cross-validation method and Lasso regularisation are adapted to obtain the model of superior generalisation ability and better persuasive results. Lastly, the results of RFR and MARS models were compared and evaluated in accordance with the goodness of fit, running time and interpretability. The results show that the RFR model performs better than the MARS in terms of fitting and operational efficiency, but is short of interpretability.

KEYWORDS:

• Random forest regression
• multivariate adaptive regression splines
• pile drivability
• cross-validation
• Lasso regularisation

Acknowledgements

The corresponding author is grateful to the financial support from Natural Science Foundation of Chongqing, China (cstc2018jcyjAX0632), Venture & Innovation Support Program for Chongqing Overseas Returnees (cx2017123) as well as Chongqing Engineering Research Center of Disaster Prevention & Control for Banks and Structures in Three Gorges Reservoir Area (Nos. SXAPGC18ZD01 and SXAPGC18YB03). Special thanks are given to the reviewers for their helpful comments on the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Natural Science Foundation of Chongqing [grant number cstc2018jcyjAX0632]; Venture & Innovation Support Program for Chongqing Overseas Returnees [grant number cx2017123]; Chongqing Engineering Research Center of Disaster Prevention & Control for Banks and Structures in Three Gorges Reservoir Area [grant number SXAPGC18YB03, SXAPGC18ZD01].