Results 81 to 90 of about 412 (171)

Predicting Model of Rockburst Based on Nondeterministic Theory [PDF]

, 2017
Predicting is the basis of prevention and controlling of rockburst hazards. Duo to the characteristic of sudden, disruptive, and complex, the accurate prediction of rockbursts is difficult and an urgent problem need to be solved. Rockburst tendency is an important metric to quantify the risk and potential intensity of occurrences and grade the hazard ...
openaire   +1 more source

LSTM-based rockburst risk prediction model incorporating attention mechanism and convolutional neural networks and its applications

矿业科学学报
Rockburst is particularly hazardous, rendering the accurate prediction of rockburst risks one of the urgent challenges in this field of research. This study proposes and establishes a long short-term memory (LSTM) rockburst risk prediction model (CAL ...
ZHANG Yu, WANG Junchao, GUO Zhengjia, FANG Kongyi   +3 more
doaj   +1 more source

Comparative analysis and application of rockburst prediction model based on secretary bird optimization algorithm

Frontiers in Earth Science
The accurate rockburst prediction is crucial for ensuring the safety of underground engineering construction. Among the various methods, machine learning-based rockburst prediction can better solve the nonlinear relationship between rockbursts and ...
Tengjie Yang, Tengjie Yang, Xinqiang Gao, Xinqiang Gao, Lichuan Wang, Yongqing Xue, Haobo Fan, Zhengguo Zhu, Zhengguo Zhu, Jingbo Zhao, Beiyi Dong, Beiyi Dong   +11 more
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Novel rockburst prediction criterion with enhanced explainability employing CatBoost and nature-inspired metaheuristic technique

Underground Space
Rockburst is a major challenge to hard rock engineering at great depth. Accurate and timely assessment of rockburst risk can avoid unnecessary casualties and property losses.
Yingui Qiu, Jian Zhou
doaj   +1 more source

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

Experimental Investigations on Multi-means and Synergistic Prediction for Rockburst [PDF]

, 2017
Based on the daily precursor monitoring techniques, such as infrared and AE / MS monitoring, methods of IRR, LURR, TDF, information entropy, TM, parameter b, MS event were used to analyze the precursory information for the predicting key point respectively.
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Deep Learning in Rockburst Intensity Level Prediction: Performance Evaluation and Comparison of the NGO-CNN-BiGRU-Attention Model

Applied Sciences
Rockburst is an extremely hazardous geological disaster. In order to accurately predict the hazardous degree of rockbursts, this paper proposes eight new classification models for predicting the intensity level of rockbursts based on intelligent ...
Hengyu Liu, Tianxing Ma, Yun Lin, Kang Peng, Xiangqi Hu, Shijie Xie, Kun Luo   +6 more
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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
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Rockburst prediction and early warning for a highway tunnel excavated by TBM based on microseismic monitoring

Frontiers in Earth Science
A newly developed microseismic (MS) monitoring system was employed in the Tianshan-Shengli tunnel to detect MS activities and then predict and provide early warning of rockburst disasters.
Jian Zhao, Dan Huang, Yongshun Cai, Dengxia Huang, Xiaolong Zhou, Fei Wang, Yuxiang Pan   +6 more
doaj   +1 more source

Early estimation method of rockburst and large deformation of surrounding rock based on the deep borehole test. [PDF]

Heliyon, 2023
Cui K, Yang Z.
europepmc   +1 more source