Results 111 to 120 of about 4,719,386 (185)

Prediction of TBM tunneling parameters and rockburst grade based on CNN-LSTM model

Meitan kexue jishu
In order to improve the intelligent construction and disaster prediction capabilities of TBM in traffic water conservancy and deep coal mine engineering, the CNN-LSTM model combining the advantages of convolutional neural network (CNN) and long short ...
Ke MAN, Liwen WU, Xiaoli LIU, Zhifei SONG, Kena LI   +4 more
doaj   +1 more source

Issue Information

Deep Underground Science and Engineering, Volume 5, Issue 2, Page i-ii, June 2026.
wiley   +1 more source

Dynamic Fault Tree–Markov Model for Rockburst Risk Assessment in Phosphate Mining

Applied Sciences
Deep phosphate mining operations face complex, dynamic working conditions characterized by the superimposed disturbances of high temperature, high stress, and high strain.
Lijing Luo, Yanling Wu, Minbo Zhang, Xiaoqian Yang   +3 more
doaj   +1 more source

Approach for Microseismic Monitoring Data-Driven Rockburst Short-Term Prediction Using Deep Feature Extraction and Interpretable Coupling Neural Networks

Applied Sciences
Rockburst disasters have become increasingly prevalent as distinct forms of subsurface geotechnical engineering advanced to the deep earth. Confronted with such a threatening subsurface geopressure disaster that poses a risk to personnel and equipment ...
Shirui Wang, Lian-Ku Xie, Yi-Meng Song, Peng Liu, Yuan Gao, Guang Zhang, Yang Yuan, Shu-Kai Jin, Zhong-Yu Wang   +8 more
semanticscholar   +1 more source

Comprehensive Prediction and Discriminant Model for Rockburst Intensity Based on Improved Variable Fuzzy Sets Approach

, 2019
Rockburst intensity prediction is one of the basic works of underground engineering disaster prevention and mitigation. Considering the dynamic variability and fuzziness in rockburst intensity prediction, variable fuzzy sets (VFS) are selected for ...
Lei Nie, Tao Zhang, Yuzheng Wang, Yan Lv, Hong Wang, Chao Du, Yan Xu, Yuanyuan He   +7 more
core   +1 more source

Rockburst Severity Prediction Under Few-Shot Conditions Using Feature Fusion Learning

Natural Resources Research
Accurate and efficient prediction of rockburst severity is crucial for the safe construction of deep rock engineering, but only a limited number of recorded rockburst data cannot enable the reliable prediction.
Xin Cai, Liye Chen, Yi-Chao Rui, Rui-Shan Cheng, Zi-Long Zhou, Ji-Xiong Zhang, Hang Yuan, Yunmin Wang   +7 more
semanticscholar   +1 more source

Rockburst Damage Scale Prediction in Underground Mines Using SMOTE-Based Resampling and Ensemble Learning

Rockburst risk in seismically active mines poses a significant threat to underground safety. This study aims to improve the prediction of rockburst-induced damage by addressing the challenge of class imbalance, which is commonly encountered in rockburst ...
Kairat Sarsembayev, Rashid Afshar, Candan Gokceoglu, Amoussou Coffi Adoko   +3 more
core   +1 more source

Pseudo-prospective and prospective rock failure along with rockburst prediction based on acoustic emission

Geohazard Mechanics
Xiang-Chun Li, Xue-Fei Zhuo, Biao Li, Qi Zhang, Saeed Vadiee   +4 more
semanticscholar   +1 more source

A BZOA-FS-Based Prediction Model for Rockburst Hazard Levels

Feature selection plays an important role in improving classification performance for high-dimensional datasets. To address the limitations of the original Zebra Optimization Algorithm (ZOA) in binary optimization problems, a Binary Zebra Optimization ...
Yan-Bin Wang, Kang-Ping Wang
core   +1 more source

Rockburst prediction using machine learning: Comparative study of algorithms and features

Smart Underground Engineering
Rui-Lang Cao, Haohan Xiao, Jiuping Li, Zuyu Chen, Si-Yang Chen, Xiaonan Wang   +5 more
semanticscholar   +1 more source