Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods

By Jian Zhou, Xibing Li, H. Mitri. Journal of computing in civil engineering, 2016

370 citations recorded by the source corpus.

Subfields: Mechanical Engineering, Mechanics of Materials, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/4ceb589c609d819bcc77524ab49d0c5e8625fa74

How researchers see this paper

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

In Mechanical Engineering

1. Jian Zhou

In Mechanics of Materials

1. Chuanqi Li
2. Yingui Qiu
3. Manoj Khandelwal
4. Wengang Zhang
5. Jian Zhou

In Ocean Engineering

1. Chuanqi Li
2. Yingui Qiu
3. Manoj Khandelwal
4. Wengang Zhang
5. Jian Zhou

Citations in the corpus

• Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration (2021, methodology): “XGBoost based on the theory of classification and regression tree can be a very effective method of regression and classification problems [9, 29, 39, 45, 55, 61, 62, 66, 67, 69, 70].”
• Assessment of pile drivability using random forest regression and multivariate adaptive regression splines (2019, methodology): “When these methods were compared, the RF method was considered the best (Zhou, Li, and Mitri 2016).”