An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines

By A. Goh, Wengang Zhang. 2014

120 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/36b03b47e072c1dc7b3bcaa61e3ee50c47ea1207

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

Citations in the corpus

• Assessment of pile drivability using random forest regression and multivariate adaptive regression splines (2019, background): “…of asphalt mixtures (Mirzahosseini et al. 2011), capacity of suction caisson in clay (Samui, Das, and Kim 2011), predicting liquefactioninduced lateral spread (Goh and Zhang 2014), analysis of geotechnical engineering systems (Zhang et al. 2015a, 2015b; Zhang, Zhang, and Goh 2017; Goh et al. 2018).”
• Multivariate adaptive regression splines and neural network models for prediction of pile drivability (2016, methodology): “…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).”
• 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 exca-vations, 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).”
• The promise of implementing machine learning in earthquake engineering: A state-of-the-art review (2020, methodology): “…of lateral spread prediction under soil liquefaction, researchers tended to acknowledge the chaotic response of liquefying soil masses to earthquakes, and most of the ML models indicated that the predicted displacements lie between one-half and twice the measured counterparts (Goh and Zhang, 2014).”