Research Article
Rockburst Prediction of Multi-dimensional Cloud Model Based on Improved Hierarchical and Critic
@INPROCEEDINGS{10.1007/978-3-030-63941-9_44, author={Xiaoyue Liu and Wei Yang}, title={Rockburst Prediction of Multi-dimensional Cloud Model Based on Improved Hierarchical and Critic}, proceedings={6GN for Future Wireless Networks. Third EAI International Conference, 6GN 2020, Tianjin, China, August 15-16, 2020, Proceedings}, proceedings_a={6GN}, year={2021}, month={1}, keywords={Rockburst Prediction Analytic hierarchy process CRITIC method Multidimensional cloud model}, doi={10.1007/978-3-030-63941-9_44} }- Xiaoyue Liu
Wei Yang
Year: 2021
Rockburst Prediction of Multi-dimensional Cloud Model Based on Improved Hierarchical and Critic
6GN
Springer
DOI: 10.1007/978-3-030-63941-9_44
Abstract
In high terrestrial stress regions, rockburst is a major geological disaster influencing underground engineering construction significantly. How to carry out efficient and accurate rock burst prediction remains to be solved. Comprehensively consider the objective information of the index data and the important role of subjective evaluation and decision-making in rockburst prediction, and use the improved analytic hierarchy process and the CRITIC method based on index correlation to obtain the subjective and objective weights of each index, and obtain comprehensive weights based on the principle of minimum discriminant information. The original cloud model and the classification interval of the forecast index were modified to make up for the lack of sensitivity of the original cloud model to the average of the grade interval. A hierarchical comprehensive cloud model of each index was generated through a cloud algorithm. Finally, the reliability and effectiveness of the model were verified through several sets of rockburst examples, and compared with the entropy weight-cloud model, CRITIC-cloud model and set pair analysis-multidimensional cloud model. The results show that the model can describe various uncertainties of interval-valued indicators, quickly and effectively determine rockburst severity.
