Multivariate adaptive regression splines for analysis of geotechnical engineering systems

By Wengang Zhang, A. Goh. 2013

335 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Management, Monitoring, Policy and Law, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/5ffa0206b36414eeba48021b966b9c00a5f11aa0

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. Lin Wang
7. Zhong-qiang Liu
8. Runhong Zhang

In Management, Monitoring, Policy and Law

1. Chongzhi Wu
2. Wengang Zhang
3. Yongqin Li
4. Lin Wang
5. A. Goh

In Safety, Risk, Reliability and Quality

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

Citations in the corpus

• Assessment of pile drivability using random forest regression and multivariate adaptive regression splines (2019, methodology): “The commonly used method, ANN has been criticised for the lengthy training process, since the optimal configuration is not known a priori (Zhang and Goh 2013).”
• Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018, background): “With piecewise linear functions: max 0; x tð Þ where a knot occurring at the position t (Zhang and Goh 2013).”
• Multivariate adaptive regression splines and neural network models for prediction of pile drivability (2016, background): “…lateral spread, and assessing the ultimate and serviceability performances of underground caverns (Attoh-Okine et al., 2009; Mirzahosseini et al., 2011; Samui, 2011; Samui and Karup, 2011; Samui et al., 2011; Zarnani et al., 2011; Lashkari, 2012; Zhang and Goh, 2013, 2014a, b; Goh and Zhang, 2014).”