基于组合赋权-改进集对分析的岩爆倾向性预测研究

岩爆是地下工程中的主要地质灾害之一，具有强破坏性、不确定性、突发性等特点。为准确、有效地预测地下工程中的岩爆倾向性，基于组合赋权-改进集对分析理论，建立了岩爆倾向性预测模型。首先从岩性、应力、围岩3个方面确立预测指标；其次利用组合数有序加权平均算子赋权法（Combination Weighting Averaging，C-OWA）、关联准则重要性赋权法（Criteria Importance Though Intercrieria Correlation，CRITIC）、博弈论分别计算出主观权重、客观权重，组合权重；最后将传统集对分析理论中的三元联系度改进为四元联系度，用改进后的集对分析理论计算出综合联系度来预测出岩爆等级。将该模型应用于西藏甲玛铜多金属矿等工程的岩爆倾向性预测中，得出该矿的综合联系度为[u1=-0.302 9]，为弱岩爆，与实际相符。研究表明：所建立的模型适用于岩爆倾向性预测，与交互式多准则决策预测模型和逼近理想解排序法预测模型对比，准确性、可靠性较优，为岩爆倾向性预测提供了一种新的途径。

Study on Rockburst Tendency Prediction Based on Combined Weighting-Improved Set Pair Analysis

Rockburst is one of the major geological hazards in underground engineering.It has strong damage，uncertaintyand suddenness.In order to accurately and effectively predict the tendency of rockburst in underground engineering，a combination weighting-improving set pair analysis prediction model was established.Firstly，predictive indicators were established from three aspects including lithology，stress and surrounding rocks.Secondly，the subjective weight，objective weightand combination weight were calculated by using the C-OWA operator weighting method，CRITIC weighting method and game theory，respectively.Finally，the ternary connection degree in the traditional set pair analysis theory was improved to a quaternary connection degree， and the improved set pair analysis theory was used to calculate the comprehensive connection degree to predict the rockburst level.The model was applied to the prediction of rockburst tendency of Jiama copper polymetallic deposit in Tibet. The calculated comprehensive connection degree （[μ1=-0.302 9] ） suggested the rockburst level of the mine was weak， which was consistent with the reality.The study results show that the established model is suitable for prediction of rockburst propensity.Compared with interactive multicritia decision makeing（TODIM）model and technique for order preference by similarity to an ideal solution（AHP-TOPSIS）model，it has better accuracy and reliability，which provides a new method for rockburst tendency prediction.