基于改进CRITIC-G1法组合赋权云模型的高阶段充填体稳定性分析

针对高阶段充填体稳定性风险评估过程中存在模糊性与随机性的特点，引入云模型理论，建立了高阶段充填体稳定性评判模型。以安庆铜矿等国内4座深井矿山为研究对象，选取坍落度、侧向暴露面积、充填料浆坡度、充填接顶效果等16项影响因素作为风险评估指标，借助云理论计算隶属于不同风险等级的各指标云模型参数，同时基于改进CRITIC-G1法，综合考虑指标之间的相关性与专家的理性判断，将主客观权重进行优化组合，最终根据评估指标的云数字特征和组合权重确定4座矿山充填体稳定性等级。研究结果表明，安庆铜矿、李楼铁矿、冬瓜山铜矿、司家营铁矿风险等级分别为Ⅳ级、Ⅳ级、Ⅲ级、Ⅱ级，司家营铁矿充填体相对更稳定。与其他数学模型相比，该模型在保证结果准确的同时，利用云数字表征模糊性与随机性从而获得不同评估指标耦合作用下的充填体稳定性等级，实现了等级的可视化，具有一定的可靠性，为高阶段充填体稳定性风险评估提供了一种新方法。

Stability analysis of high stage backfill based on improved CRITIC-G1 method combined with weighted cloud model

The process of high-stage filling stability risk assessment is characterized with fuzziness and randomness. A high-stage filling stability evaluation model with the introduction of the cloud model theory is proposed in this paper. Taking four domestic deep mines including Anqing Copper Mine as the research objects, 16 influential factors, such as slump, lateral exposure area, filling slurry slope, and filling effect, were selected as risk assessment indicators. The cloud model parameters of each index belonging to different risk levels were calculated with the help of cloud theory. At the same time, based on the improved CRITIC-G1 method, the subjective and objective weights of the evaluation indexes were optimized and integrated into the comprehensive weights of the indexes. Then, the comprehensive certainty degree of the backfill stability was calculated by using the forward Gaussian cloud generator, and the stability grade of the filling body of four mines was finally determined. The results show that the risk levels of Anqing Copper Mine, Lilou Iron Mine, Dongguashan Copper Mine and Sijiaying Iron Mine are grade IV, grade IV, grade III, and grade II, respectively, and the filling body of Sijiaying Iron Mine is relatively stable. Compared with other mathematical models, the proposed model not only ensures the accuracy of the results, but also uses cloud numbers to represent the fuzziness and randomness so as to obtain the stability grade of the filling body under the coupling effect of different evaluation indexes. The model realizes the visualization of the classification, and has certain reliability, providing a new method for the risk assessment of the stability of high-stage filling body.