A new hybrid model of information entropy and unascertained measurement with different membership functions for evaluating destressability in burst-prone underground mines

The occurrence of unpredictable hazards are frequent with the increased depth of mining, especially the hazards caused by stress concentration. In order to mitigate the negative effectiveness results from mining-induce stress, various approaches have been employed in underground mines. Destress blasting, as an efficient method, has gained a lot of popularity in recent years. However, it is crucial to estimate the destressability of specific area before conducting destress blasting. In this study a combination model on the basis of both unascertained measurement (UM) and entropy coefficients was applied to observe the performance of destressability evaluation. Eight representative parameters, i.e., stiffness of the rock mass, brittleness of the rock mass, degree of fracturing, proximity to failure, destress blast orientation, width of the target zone, unit explosive energy, and confinement of the charges were chosen as initial input parameters, and their membership distributions were described by four types of membership methodologies, i.e., line, parabolic curve, exponential curve, and sine curve. Meanwhile, the weights of each index could be computed based on the single measurement matrix. Then, destressability of the samples was easily identified with Euclidean distance and comprehensive measurement vectors which were computed by single measurement vectors and weight coefficients. Finally, it was found that the assessment results are in accordance with those calculated by destressability index. It can be concluded that the proposed hybrid model is able to eliminate the disturbance of subjective factors and ensure the reliability of these outcomes. At the same time, it can provide a novel idea/process for the destressability evaluation.

Automated summary

The study applies a hybrid model based on unascertained measurement and entropy coefficients to evaluate the destressability of underground mines. By selecting various parameters, computing weights and comprehensive measurement vectors, the destressability can be accurately assessed. The results show that the proposed model can eliminate subjective factors and ensure the reliability of the evaluation, providing a new idea/process for destressability evaluation.