A Connection Cloud Model Coupled With Improved Conflict Evidence Fusion Method for Prediction of Rockburst Intensity

Abstract

As a common geological hazard in underground engineering, rockburst has many uncertain factors and interactions, and is of a complicated mechanism. Based on the information fusion theory, a methodology using a novel connection cloud model, which can overcome the defect of the traditional cloud model requiring factors to be normal distribution when determining the basic probability assignment function of the evidence is proposed for predicting the rockburst intensity. And concepts of Lance-distance and Deng-entropy are also introduced here to improve the traditional evidence fusion algorithm. The practical application shows that the result obtained from the proposed model is consistent with that in the actual site testing, so this model is feasible and effective for the prediction of the rockburst intensity. Furthermore, compared with results from other six improved evidence fusion rules, the improved evidence fusion method proposed here has higher fusion convergence precision, faster convergence rate, and more accurate and reliable results, this may provide a new reference for other similar uncertain problems.

Rockburst intensity prediction in underground buildings based on improved spectral clustering algorithm

“…Obviously, the spectral clustering that uses the proposed improved K-medoids outputs more accurate clustering results compared to the other two under the same scale parameter; the success rate of the proposed improved flow is also higher; once two or more clusters obtain the same label, the flow will stop and Frontiers in Earth Science frontiersin.org return an error, current clustering is declared failed, which is a difficult strategy compared to other soft classification strategies (Chen et al, 2021), which is also the reason why some points are missed in the aforementioned figure. In fact, most confusion matrices of failed clustering results show that there is some difficulty in the effective discrimination of samples under light (L) and moderate (M) conditions, which is also an issue in other rockburst intensity prediction methods.…”