Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Predicting intact rock strength for mechanical excavation in dry and saturated condition using multivariate statistics and artificial neural networks optimized using genetic algorithm
Saved in:
| Published in: | Journal of Earth System Science |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2026
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
Similar Items: Predicting intact rock strength for mechanical excavation in dry and saturated condition using multivariate statistics and artificial neural networks optimized using genetic algorithm
- Physics‐Informed Bias Correction Using a Convolution‐Based Multivariate Gaussian Process
- Application of swarm intelligence optimization techniques and explainable data-driven methods in predicting the shear strength of serrated jointed rock masses
- Image-based prediction of particle size distribution in recycled stabilized soils using convolutional neural networks
- Ultrasonic shear wave response to mechanical parameters in single-jointed rock masses: an experimental investigation of rock fragmentation effects
- Limited influence of bedrock strength on river profiles: the dominant role of sediment dynamics
- Cement-stabilized marine clay under thermal cycling: strength, suction, and microstructure