A machine learning-based method for predicting the shear behaviors of rock joints
In this study, machine learning prediction models (MLPMs), including artificial neural network (ANN), support vector regression (SVR), K-nearest neighbors (KNN), and random forest (RF) algorithms, were developed to predict the peak shear stress values and shear stress-displacement curves of rock joi...
Saved in:
| Main Authors: | Liu He, Yu Tan, Timothy Copeland, Jiannan Chen, Qiang Tang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Soils and Foundations |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0038080624000957 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A shear brittleness index for rock joints and its application in direct shear failure analysis
by: Sheng Li, et al.
Published: (2025-09-01) -
Experimental and Numerical Simulation Study on the Shear Behavior of Rock-like Specimens with Non-Persistent Joints
by: Gang Wang, et al.
Published: (2024-12-01) -
Investigation on Relationship between Failure Patterns and Shear Strength of Rock Joints
by: CHEN Hao-xiang, WANG Ming-yang, JIN Tian-wei, QI Cheng-zhi, YI Yue-tong
Published: (2025-06-01) -
Study on the shear characteristics and macro scopic mechanism of rock joints under cyclic loading conditions
by: WANG Gang, et al.
Published: (2025-06-01) -
Influence of rock bolt reinforcement on shear behaviour of a nonpersistent joint plane
by: Hongyun Xue, et al.
Published: (2024-12-01)