D-P-Transformer: A Distilling and Probsparse Self-Attention Rockburst Prediction Method
Energies, 2022 Rockburst may cause damage to engineering equipment, disrupt construction progress, and endanger human life. To this day, the occurrence of rockburst remains complex and difficult to predict.
Yu Zhang +4 more
doaj +1 more source
Study on Rockburst Tendency of Deep Underground Engineering based on Multi-Factor Influence
Electronic Journal of Structural Engineering, 2023 Rockburst disaster seriously threatens the construction schedule of underground tunnel engineering and the safety of construction workers. Rockburst prediction has become one of the critical methods for evaluation of surrounding rock stability and safe ...
Zihui Zhu, Feiyue Sun, Jiaqi Guo
doaj +1 more source
Rockburst Intensity Classification Prediction Based on Multi-Model Ensemble Learning Algorithms
Mathematics, 2023 Rockburst is a common and huge hazard in underground engineering, and the scientific prediction of rockburst disasters can reduce the risks caused by rockburst.
Jiachuang Wang, Haoji Ma, Xianhang Yan
doaj +1 more source
Application research of DHNN model in prediction of classification of rockburst intensity
Gong-kuang zidonghua, 2018 In view of problems of randomness and subjectivity in determining weight of existing rockburst prediction methods，a discrete Hopfield neural network (DHNN) model for prediction of classification of rockburst intensity was proposed。The model selects ...
XU Jia +5 more
doaj +1 more source
Research on Rockburst Prediction Classification Based on GA-XGB Model [PDF]
IEEE Access, 2021 Rockburst is a typical engineering geological disaster under the condition of high geostress. The rockburst classification and prediction are of great significance for the prevention and control of engineering geological disasters under the high geostress environment, and can reduce or even avoid the loss of personnel, equipment, and property.
Xuebin Xie, Wei Jiang, Jiang Guo
openaire +2 more sources
Rockburst Prediction Based on the KPCA-APSO-SVM Model and Its Engineering Application
Shock and Vibration, 2021 The progress of construction and safe production in mining, water conservancy, tunnels, and other types of deep underground engineering is seriously affected by rockburst disasters. This makes it essential to accurately predict rockburst intensity.
Yuefeng Li +5 more
doaj +1 more source
Prediction of Rockburst Risk in Coal Mines Based on a Locally Weighted C4.5 Algorithm
IEEE Access, 2021 Rockburst is a dynamic phenomenon characterized by the sudden, abrupt, and violent release of deformation energy in coal and rock masses around mine shafts and slopes that can result in considerable destruction.
Yanbin Wang
doaj +1 more source
Classification Prediction of Rockburst in Railway Tunnel Based on Hybrid PSO-BP Neural Network
Geofluids, 2022 Rockburst is one of the main disasters in railway tunnel construction. In order to accurately predict the rockburst intensity level of the railway tunnel, the rock stress coefficient σθ/σc, rock brittleness coefficient σc/σt, and elastic energy index Wet
Min Zhang
doaj +1 more source
A new empirical chart for rockburst analysis in tunnelling: Tunnel rockburst classification (TRC)
International Journal of Mining Science and Technology, 2021 Rockburst is defined as a phenomenon with immediate dynamic instability under excavation unloading conditions of deep or high geostress areas. Inadequate knowledge and lack of characterizing information prevent engineers and experts from achieving ...
Hadi Farhadian
doaj +1 more source
An Intelligent Rockburst Prediction Model Based on Scorecard Methodology [PDF]
Minerals, 2021 Rockburst is a serious hazard in underground engineering, and accurate prediction of rockburst risk is challenging. To construct an intelligent prediction model of rockburst risk with interpretability and high accuracy, three binary scorecards predicting different risk levels of rockburst were constructed using ChiMerge, evidence weight theory, and the
Honglei Wang +5 more
openaire +1 more source