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

By Wengang Zhang, Chongzhi Wu, Yongqin Li, Lin Wang, Pijush Samui. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2019

187 citations recorded by the source corpus.

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.

Subfields: Civil and Structural Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/c33ee89c7188866faa6b5b14a6dd2b731d57c94c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Chongzhi Wu
2. Wengang Zhang
3. Yongqin Li
4. Han-long Liu
5. A. Goh
6. Zhong-qiang Liu
7. Runhong Zhang
8. S. Lacasse

In Safety, Risk, Reliability and Quality

1. Chongzhi Wu
2. Yongqin Li
3. Wengang Zhang
4. A. Goh
5. Han-long Liu

Citations in the corpus

• Application of deep learning algorithms in geotechnical engineering: a short critical review (2021, methodology): “And the transfer learning model of mineral microscopic images is also established based on Inception-v3 architecture considering the task of microscopic mineral image identifica-tionis tedious and time-consuming in the lab (Zhang et al. 2019e).”
• State-of-the-art review of soft computing applications in underground excavations (2020, methodology): “As for the detailed introduction of MARS algorithm, the model development procedures as well as applications in underground excavations, please refer to Zhang and Goh (2013, 2016a, 2016b), Goh and Zhang (2014), Goh et al. (2017, 2018), and Zhang et al. (2017a, 2017b, 2019).”