Rockburst prediction model based on comprehensive weight and extension methods and its engineering application

• Published in: Bulletin of engineering geology and the environment 2020-11, Vol.79 (9), p.4891-4903
• Main Authors: Zhang, Lewen, Zhang, Xiangyu, Wu, Jing, Zhao, Dukun, Fu, Hao
• Format: Article
• Language: English
• Subjects: Earth and Environmental Science; Earth Sciences; Foundations; Geoecology/Natural Processes; Geoengineering; Geotechnical Engineering & Applied Earth Sciences; Hydraulics; Nature Conservation; Original Paper
• ISSN: 1435-9529; 1435-9537
• DOI: 10.1007/s10064-020-01861-4

In view of the dynamic instability of rock mass in high geostress areas during underground engineering excavation, the comprehensive weight and extension methods are adopted to research the rockburst prediction. Firstly, five main influencing factors including uniaxial compressive strength, stress coefficient, brittleness coefficient, elastic energy index, and integrity of rock mass are used as the evaluation indexes of rockburst prediction according to the conditions required for rockburst occurrence. The assessment index system of rockburst intensity is constituted. Secondly, the analytic hierarchy process (AHP) and variation coefficient methods are used to determine the comprehensive weight of evaluation index, and the rockburst prediction model is established based on the extension evaluation method. Thirdly, the parameter programming and numerical calculation of the proposed prediction model are carried out in the MATLAB software. The user visualization execution window and software system of rockburst prediction model are realized. Finally, the software system is applied to the rockburst prediction in the water diversion tunnels of Jiangbian hydropower station and Jinping secondary hydropower station. The prediction results are compared with the actual situation and other evaluation methods. The results show that (i) the establishment of the user visualization window realizes the visualization and systematization of rockburst prediction model, which improves the data import rate and calculation efficiency. (ii) The prediction results of the proposed software system agree well with the actual situation, and they are more accurate than other evaluation methods. (iii) The proposed software system of rockburst prediction can also be used in coal mine, metro, and other underground projects, which has good engineering application values.