Abstract:

To address the issues of strong subjectivity in weight allocation and the limitations of single-weighting methods in traditional rock mass evaluation, a composite rock mass quality assessment model was developed. This model integrated the G1 method, the CV method, and the TOPSIS. In this framework, subjective weights were assigned to evaluation indicators using the G1 method based on their importance ranking. Concurrently, objective weights were calculated by the CV method according to the dispersion of the data. The Euclidean distance was then applied to optimize and integrate these subjective and objective weights, resulting in a comprehensive weight index. Based on this integrated index, the TOPSIS method was used to calculate the distance between each sample and the positive/negative ideal solutions. The quantitative ranking and classification of rock mass quality were achieved by assessing the relative proximity. When applied to an open-pit mine in Sichuan Province and compared with four other evaluation methods, the rock mass quality in the surveyed area was primarily classified as Grade Ⅱ by the G1-CV-TOPSIS model, with localized areas identified as Grade Ⅲ and Grade Ⅴ. It is demonstrated through comparison that the proposed G1-CV-TOPSIS model effectively integrates subjective experience with objective data, thereby significantly improving the accuracy and scientific rigor of rock mass quality assessment.

Key words: method of order relation (G1), coefficient of variation (CV), technique for order preference by similarity to an ideal solution (TOPSIS), rock mass evaluation, combined weighting